# A Review of the Applications of Deep Learning-Based
Emergent Communication

Brendon Boldt<EMAIL_ADDRESS>
Language Technologies Institute
Carnegie Mellon University, Pittsburgh, PA, USA David Mortensen
<EMAIL_ADDRESS>
Language Technologies Institute
Carnegie Mellon University, Pittsburgh, PA, USA

###### Abstract

Emergent communication, or emergent language, is the field of research which
studies how human language-like communication systems emerge _de novo_ in deep
multi-agent reinforcement learning environments. The possibilities of
replicating the emergence of a complex behavior like language have strong
intuitive appeal, yet it is necessary to complement this with clear notions of
how such research can be applicable to other fields of science, technology,
and engineering. This paper comprehensively reviews the applications of
emergent communication research across machine learning, natural language
processing, linguistics, and cognitive science. Each application is
illustrated with a description of its scope, an explication of emergent
communication’s unique role in addressing it, a summary of the extant
literature working towards the application, and brief recommendations for
near-term research directions.

##  1 Introduction

Deep learning-based methods in natural language processing and multi-agent
reinforcement learning provide a powerful way simulate how human language-like
communication systems emerge _de novo_. This area of research is called
_emergent communication_ or _emergent language_. Multi-agent reinforcement
learning-based systems like AlphaZero (Silver et al., 2017) and OpenAI’s hide-
and-seek agents (Baker et al., 2020) have leveraged self-play to exhibit
convincing examples of complex behavior emerging from basic environment
dynamics. Such deep reinforcement learning techniques were applied to discrete
communication systems starting in 2016 and 2017 with papers like Foerster et
al. (2016); Lazaridou et al. (2016); Havrylov & Titov (2017); Mordatch &
Abbeel (2018). Although replicating as complex a behavior as human language is
intuitively important, it is necessary to complement such notions with clear
directives as to how it could apply to other areas of science, technology, and
engineering.

Thus, this work is a review of the most salient goals and applications of deep
learning-based emergent communication research. We illustrate each of the
applications by providing a description of its scope, an explication of
emergent communication’s unique role in addressing it, a summary of the extant
literature working towards the application, and brief recommendations for
near-term research directions. This work has three primary goals. (1) This
work is meant to inspire future emergent communication research by compiling
the most salient areas of research into a single document with relevant work
cited. (2) It illustrates to practitioners outside of emergent communication
the potential ways that emergent communication can be used in an easily-
referenced format. (3) It define the ultimate aims of emergent communication,
which is critical to guiding the field of research through practices like
establishing evaluation metrics and benchmarks. Evaluation metrics require
explicitly defining what a _good_ or _desirable_ emergent language is, and
understanding what emergent communication can be used for is a foundational
step in their development.

Figure 1: Structure of the applications discussed in this review. Applications
of Emergent Communication Internal Tooling Rederiving Human Language Metrics
Theoretical Models Task-Driven Synthetic Data Multi-Agent Communication
Interacting with Humans Explainable ML Models Knowledge-Driven Cognitive
Science Perception Representations Evolution of Language Language Acquisition
Linguistic Variables Phonology Morphology Syntax Semantics Compositionality
Pragmatics Sociology Section 4Section 4.6Section 4.5Section 4.3Section
4.2Section 2Section 2.1Section 2.2Section 2.3Section 2.4Section 3Section
3.1Section 3.2Section 3.3Section 3.4

###### Contents

  1. 1 Introduction
    1. 1.1 Example of emergent communication system
    2. 1.2 Scope
    3. 1.3 Related work
    4. 1.4 Structure of review
  2. 2 Internal Goals
    1. 2.1 Rederiving human language
    2. 2.2 Metrics for emergent communication
    3. 2.3 Theoretical models
    4. 2.4 Tooling
  3. 3 Task-Driven Applications
    1. 3.1 Synthetic language data
    2. 3.2 Multi-agent communication
    3. 3.3 Interacting with humans
    4. 3.4 Explainable machine learning models
  4. 4 Knowledge-Driven Applications
    1. 4.1 General paradigm of knowledge-driven applications
    2. 4.2 Language, cognition, and perception
    3. 4.3 Origin of language
    4. 4.4 Language change
    5. 4.5 Language acquisition
    6. 4.6 Linguistic variables
  5. 5 Discussion
    1. 5.1 Quantitative summary of results
    2. 5.2 Internal goals
    3. 5.3 Task-driven applications
    4. 5.4 Knowledge-driven applications
  6. 6 Conclusion
  7. A Review Methods
    1. A.1 Collecting papers
    2. A.2 Goal categorization
  8. B Complete List of Reviewed Papers

###  1.1 Example of emergent communication system

$T_{1}$$T_{2}$3 14 1 59 2$T_{3}$ (a) $T_{1}$: The sender (left) observes an
object. $T_{2}$: The sender passes a message to the receiver (right). $T_{3}$:
The receiver chooses from a handful of candidate objects.

Observation vector Sender RNN conditioned on observation Message sequence of
one-hot vectors Candidate Observations set of vectors Receiver RNN conditioned
on message and observations Prediction prob. dist. over observations Reward
real value (b) Illustration of the technical architecture of the signaling
game.

Figure 2: An illustration of the discrimination variant of the signaling game,
one of the simplest and most common environments in emergent communication
research.

In this section, we briefly illustrate a canonical example of an emergent
communication game, namely discrimination variant of the signaling game
(Lewis, 1969). The signaling game is one of the simplest and most common
emergent communication games in the literature, and many further games and
environments can be conceptualized as extensions of the signaling game. As
seen in Figure 2(a), the basic signaling game involves two agents, a sender
and receiver. In a single round of the game, the sender first observes an
object, then sends a message to the receiver, and finally the receiver chooses
an object after observing a set of candidate objects along with the message
from the sender. The round is successful if the receiver chooses the object
which corresponds to the original observation made by the sender.

The technical architecture of the signaling game is illustrated in Figure
2(b). The initial observation made by the sender is represented by a real-
valued vector which which is an input for the neural network. The sender is a
sequence generation model conditioned on the observation vector; RNNs are a
common choice of architecture, but a number of other architectures can also be
used. The sender generates a sequence of one-hot vectors which will serve as
the “message” sent to the receiver. The receiver, typically an encoder RNN,
then takes as input both the message and the set of candidate observations.
The set of candidate observations contains both the correct observation made
by the sender as well as “distractor” observations which differ from the
correct one (i.e., like wrong answers on a multiple-choice question). Finally,
the sender and receiver receive a reward based on whether or not the receiver
selected the correct observation. This reward is then used to optimize the
sender and receiver (e.g., with gradient descent111Since the message, which is
a sequence of one-hot vectors, is discrete, it is typically required to
optimize the sender with some additional technique like REINFORCE (Williams,
1992) or Gumbel-Softmax (Jang et al., 2017; Maddison et al., 2017).).

In the beginning, there is no pre-established communication protocol; that is,
the messages produced by the sender do not “mean” anything. It is only through
the repeated trials and optimization that messages begin to take on meaning
such that the sender can effectively communicate the correct observation to
the receiver. The protocol after training is considered the “emergent
language” since it is a communication system which is the result of the
functional pressure to succeed provided by the optimization of the sender and
receiver.

###  1.2 Scope

In order to effectively select papers for the review, we need to define
particular scope of “emergent communication” that we are dealing with. We are
not claiming that work excluded by these criteria is unimportant or unrelated
to the included work, nor are we arguing that these criteria should be viewed
as normative. Rather, these criteria are merely intended to be sufficient for
conducting a complete, coherent review of a field of research. The scope of
this review specifically comprises the following criteria:

  * •

_Necessarily_ , the topic is an agent-based computer model, that is, the
simulation of individual computer agents in an environment.

    * –

_Typically_ , it uses reinforcement learning.

    * –

_Necessarily_ , it is not simply the result of training a model on human
language data (e.g., emergent properties of pre-trained language models do
_not_ qualify).

    * –

_Typically_ , the system contains multiple agents (e.g., an agent talking to
itself could still qualify).

  * •

_Necessarily_ , the agents have a communication channel.

    * –

_Necessarily_ , the communication is analogous to human language in some way.

    * –

_Typically_ , the communication channel is discrete symbols (i.e., analogous
to words or subword units).

    * –

_Sometimes_ , the communication channel may be continuous (e.g., analogous to
speech sounds), but the structure of the channel or the resulting protocol
must be of interest (i.e., an unconstrained, unstudied continuous channel does
_not_ count.)

  * •

_Necessarily_ , the exact nature of communication (e.g., the structure or
content of the protocol) is not determined ahead of time; it “emerges” from
simpler characteristics of the environments and agents.

  * •

_Necessarily_ , the approach uses deep learning methods.

    * –

_Typically_ , methods use neural networks optimized by gradient descent.

    * –

_Typically_ , work is associated with the communities of
ICLR222https://iclr.cc/, NeurIPS333https://neurips.cc/, and
ICML444https://icml.cc/ conferences and the EmeCom
workshop555https://sites.google.com/view/emecom2022.

###  1.3 Related work

This section briefly discusses some closely related areas of research that
fall outside of the scope of this paper. Although the goals and applications
of these research areas are relevant to those discussed in this paper, we do
not incorporate these into this paper in the interests of length. While their
applications are very similar to those of deep learning-based emergent
communication, the particular issues, methods, and possibilities which deep
learning techniques present are quite different from these related areas.

##### Emergent communication

Lazaridou & Baroni (2020) offer a general review of emergent communication
research. It covers the same body of literature as this paper but with a
general scope. Readers unfamiliar with the field of emergent communication
would benefit greatly by reading this review first as it covers the essential
elements of the field (background, methods, results, related work, etc.). This
paper, on the other hand, focuses specifically on the goals and applications
of emergent communication research.

A few other position papers have been published on emergent communication and
share this paper’s goal of guiding future work through a direct analysis and
discussion of the literature. LaCroix (2019); Moulin-Frier & Oudeyer (2020);
Galke et al. (2022) synthesize linguistic research on the evolution of
language with contemporary methods in emergent communication, highlighting
what aspects do not line up and how emergent communication research might
change its approach. Finally, Zubek et al. (2023) provide a more robust
critique of current methods in emergent communication from various linguistic
perspectives.

##### NLP and multi-agent RL

Some areas of natural language processing focus on learning human language by
leveraging deep multi-agent reinforcement learning in a way similar to
emergent communication. This includes approaches like Lee et al. (2019);
Cogswell et al. (2020) which use multi-agent dialog grounded with visual
referents or Lu et al. (2020) which uses iterated learning framework for
tuning dialog agents. Although the methods are similar, these approaches
typically do not care about communication systems that are emerging from
scratch and instead focus directly on improving performance with human
languages.

##### Emergent language without deep learning

Computer simulations of the emergence of language are also possible without
recourse to deep learning methods. Simulations along these lines might use
other forms of machine learning or simply mathematical models of agents and
environments. For example, Werner & Dyer (1991) simulate the emergence of a
communication system in a population of mating animals. Female animals guide
the male animals towards them by emitting discrete messages. Each agent is
implemented as a connectionist artificial neural network which is optimized
with a genetic algorithm. Another instance is Kirby (2000), which verifies the
possibility of compositional communication emerging without biological
evolution. Specifically, it presents a mathematical model of an agent
population where new members must learn to communicate from older members (who
eventually die off). This is implemented as a computer program which can
empirically verify the hypothesis.

Although this research area has significant overlap in terms of goals, the
methods have significantly different challenges. In particular, methods not
based on deep learning tend to have strong inductive biases which constrain
the range of languages that can emerge. In contrast, one of the main
challenges of deep learning-based emergent communication is trying to find the
environmental pressures and functional advantages which shape language in
place alongside the weaker inductive biases of deep neural networks.

##### Emergent communication with humans

Research on the emergence of human language also takes place outside the
context of computer simulation altogether. Experimentally, small-scale studies
can be done with humans in the laboratory. For example, Kirby et al. (2008)
test the emergence of structure in language from language transmission
dynamics by having humans serve as the agents in a laboratory experiment.
Observationally, there are recorded instances of a full human language
emerging as in the case of Nicaraguan Sign Language, where deaf children with
minimal prior linguistic knowledge developed language when placed together in
a school environment (Kegl et al., 1999). Despite the relevance of this
research to deep learning-based emergent communication, the challenges of
human-base studies diverge significantly from those based on machine learning.

##### Symbol emergence in robotics

Taniguchi et al. (2015) survey a research area called “symbol emergence in
robotics” (SER). SER is concerned with developing autonomous robots with the
ability to discover meaning and communication skills from sensory-motor
experiences with humans and other robots. In this way, both SER and emergent
communication study “bottom-up” methods of autonomous agents acquiring the
ability to use language in a deep, embodied way. SER is more concerned with
the development of robotic agents which can dynamically learn to interact like
humans through pragmatic and social facets of language. In contrast, emergent
communication is more concerned with observing the entire process of language
creation in virtual environments through agents interacting with other agents.

###  1.4 Structure of review

We divide the applications of emergent communication into three broad
categories:

  * •

_Internal goals_ (Section 2) aim towards improving the technique in its own
right. In a sense, these goals are the “basic research” of emergent
communication.

  * •

_Task-driven applications_ (Section 3) aim at solving a well-defined problem.
These goals generally correlate with goals in the domain of engineering such
as those found in NLP.

  * •

_Knowledge-driven applications_ (Section 4) aim at increasing the knowledge of
some phenomenon. These goals generally correlate with the goals of sciences
such as linguistics.

We illustrate each application with four sections which each answer a
question:

  * •

“Description”: What exactly is the problem being solved?

  * •

“Applicability”: How do the techniques of emergent communication (in practice
or in theory) uniquely address this problem?

  * •

“Current state”: What progress has been made in the literature toward
addressing this problem?

  * •

“Next steps”: What does the next important research paper towards this
application look like?

We give a brief of analysis of the trends we found in reviewing the literature
in Section 5 before concluding in Section 6. Details of the review process are
presented in Appendix A, and a complete list of works surveyed is given in
Appendix B.

##  2 Internal Goals

Internal goals are not what we would typically consider applications at all
since they are focused on issues internal to the field of emergent
communication. Nevertheless, these applications are important because they are
(1) prerequisites for applying emergent communication to other areas and (2)
the primary contributions of many papers that reference these goals. While, in
a sense, any contribution could be considered an internal goal, we choose to
address the internal goals which represent the clearest and most salient
waypoints within emergent communication research.

###  2.1 Rederiving human language

##### Description

Aiming to create emergent communication that resembles human language is a
central characteristic of emergent communication and drives much of the
research on the topic. This resemblance can include everything from low-level
traits like compositional semantics and tree-like syntax to high-level traits
like implicature (pragmatics) and sociolects (sociolinguistics), although how
exactly to define this resemblance is an open question within linguistics.
Aiming for resemblance does not necessitate exact replication of human
language (or even having an exact definition of it): within human language we
see a large amount of variation upon fundamental commonalities (e.g.,
distinguishing between nouns and verbs, having distinct units of meaning which
appear in many contexts). _Rederiving human language_ , then, is the process
of developing the conditions (e.g., environment, agent architecture, games)
which produce emergent communication which resembles human language.

Rederiving human language distinct from, although related to, Origin of
language (Section 4.3) and Language acquisition (Section 4.5). Research into
the origin and acquisition of language has a primary interest in the specific
historical, environmental, and cognitive contexts of humans and their use of
language. In contrast, rederivation is only concerned with these contexts for
their instrumental value in developing emergent communication which is similar
to human language.

##### Applicability

The resemblance of emergent communication to human language is the nexus of
most other goals in the field: other goals will either work toward resemblance
in some respect or derive their effectiveness from resemblance to human
language (or both). This resemblance to human language need not be
perfect—even a partial rederivation of human language could still support many
important downstream applications.

Internal Goals (Section 2) primarily work toward the rederivation of human
language, while the task- and knowledge-based applications primarily derive
their effectiveness from the rederivation. In particular, Task-Driven
Applications (Section 3) rely on emergent communication having: structural
similarities to human language (Synthetic language data (Section 3.1)),
generalizability to new situations (Multi-agent communication (Section 3.2)),
discourse structure (Interacting with humans (Section 3.3)), and the capacity
to externally represent internal states (Explainable machine learning models
(Section 3.4)). Knowledge-Driven Applications (Section 4), for example, rely
on emergent communication resembling human language in terms of: cognitive
processes influencing linguistic behavior (Language, cognition, and perception
(Section 4.2)), macro-scale social processes (Origin of language (Section 4.3)
and Language change (Section 4.4)), mechanism of learning and acquisition
(Language acquisition (Section 4.5)), and general structure at every level
(Linguistic variables (Section 4.6)).

##### Current state

No work in the current body of literature has explicitly pursued the
rederivation of human language. There are a large number of papers that study
aspects of making emergent communication more human language-like in isolation
(almost any paper in Linguistic variables (Section 4.6) does this in some
capacity), but no papers have made steps towards rederivation holistically.
While studying just one aspect of emergent communication at a time yields more
tractable research questions, the risk is that isolating individual aspects
misses the ways in which emergent communication is truly an _emergent_
phenomenon within a complex system (Bar-Yam, 2002, Sec. 1.3). Complex systems
are characterized by non-obvious interactions among the many moving parts, and
taking away single elements of the system might change the behavior in
significant, unpredictable ways. To the extent to which this is true, studying
isolated phenomena in simple environments has limited potential.

For example, Ren et al. (2020) show, in line with established experiments with
mathematical models (Kirby et al., 2007) and human subjects (Kirby et al.,
2008), that the imperfect transmission of language from generation to
generation (i.e., _iterated learning_) can explain a bias toward
compositionality in communication system without further agent-internal
biases. Yet empirical investigation of compositionality in emergent
communication literature often uses fixed-population environments666I.e.,
environments where the set of agents remains constant throughout the training
process. Contrast this with dynamic populations where newly-initialized agents
enter the population and older agents leave the population.. The fact that
iterated learning has diverse support as an explanation for compositionality
calls into the question the results of compositionality research which does
_not_ take iterated learning into account, since iterated learning could be a
sufficient driver for compositionality in emergent communication, outweighing
other potential sources like model capacity (Resnick et al., 2020) or
perception (Lazaridou et al., 2018).

##### Next steps

The rederivation of human language in full is a massively complex task which
may be impossible in practice or even in principle. Yet even if it is possible
only to a limited degree, emergent communication can still fulfill many of its
applications. The first step toward rederiving human language is laying down
the theoretical foundations: identifying the most salient properties of human
language and using these to develop a concrete problem definition of
“rederiving human language”. The field of linguistics will be especially
important for formulating precise notions of “rederiving human language”. Such
formulations will provide the groundwork for identifying the technical issues
with rederiving human language through emergent communication techniques. For
example, we speculate that: language will need to be processed by larger
neural networks with parameter counts in the billions; agents will also need
to have realistic cognitive constraints on producing and understanding
language; populations of agents will have to number in the hundreds to mimic
even the smallest human language communities; environments will need to be
scaled up in terms of both sensory input (e.g., 3-dimensional environments,
embodiment) as well as task complexity (e.g., involving multi-step planning);
and many advanced techniques from deep reinforcement learning will need to
incorporated into the optimization process in order to learn from richer
environments (e.g., efficiently learning representations, planning, multi-
agent cooperation).

###  2.2 Metrics for emergent communication

##### Description

A metric, for our purposes, is a well-defined method for quantifying a
property of or notion about an emergent communication system. Some properties
in emergent communication are fairly concrete and are naturally quantitative
such as vocabulary size or task success rate. Other properties are more
abstract and there is not single, obvious way to quantify them (i.e., they are
underspecified in some capacity). For example, compositionality often refers
to the idea that “the meaning of a composite message is a function of the
meanings of individual parts”, but this definition is underspecified. It does
not specify if “meaning” rests in the interpretation of the speaker, listener,
or both, nor does it specify what limits might exist on functions used to
combine meaning—each interpretation would be quantified differently and may be
useful in different contexts. Finally, _evaluation_ metrics are even more
abstract as they try to directly measure how _good_ , _useful_ , or
_desirable_ something is in a general sense. For example, F-score is an
evaluation metric for classifiers; that is, a better classifier should have a
higher F-score (insofar as F-score is an effective evaluation metric), and
generally speaking, a classifier with higher F-score will be more useful than
one with a lower F-score.

Thus, developing metrics within emergent communication comprises a number of
different tasks, including: designing precise formulations of abstract
properties, developing practical computational methods for implementing these
formulations, and demonstrating mathematically and empirically that they
accurately quantify the particular property.

##### Applicability

Metrics, in general, are a ubiquitous part of research in most any area of
science or engineering. They are integral to formulating testable hypotheses
since they delineate precisely what is being considered empirically (or
theoretically). They are also what enables effective summarization and
statistical analysis of the results of experiments beyond mere qualitative
analysis. Together, these two factors make principled comparison with prior
work possible. Evaluation metrics, in particular, help identify approaches to
a given problem are most effective. These are especially important for the
long-term development of a field as they help gauge overall progress and
direct efforts towards the most promising approaches.

##### Current state (compositionality)

Metrics for compositionality and generalizability comprise the lion’s share of
literature on this goal while only a few have been developed for other
properties. This corresponds with the most common goals of emergent
communication papers which are to develop emergent communication which has
compositional semantics and generalizes beyond the scenarios seen during
training.

Compositionality (or compositional semantics) refers to the general principal
that utterances with complex meaning derive their meaning from a combination
of the meaning of the components of the utterance (e.g., a “red car” is a car
that is red). This is in contrast to “holistic” communication where there is
no relationship between the meaning of an utterance and its components. 777For
example, a “black swan” can refer (idiomatically) to a rare event—something
that is neither black nor a swan. The most popular metric for compositionality
is topographic similarity (Brighton & Kirby, 2006; Lazaridou et al., 2018),
which quantifies compositionality as the degree of correlation between
distances in the referent feature space and distances in the message space
(illustrated in Figure 3). Specifically, Lazaridou et al. (2018) use the
Spearman’s rank correlation coefficient ($\rho$) on the pairwise distances
between objects in the feature space (quantified with cosine similarity) and
their corresponding messages (quantified with Levenshtein distance). In this
sense, toposim is more of a family of metrics since the precise methods of
computing correlation and distance in the object and message spaces have a
number of concrete realizations.

$\mathcal{M}$$\mathcal{O}$ (a) Mapping with low topographic similarity; low
correlation between spaces.

$\mathcal{M}$$\mathcal{M}$$\mathcal{O}$$\mathcal{O}$ (b) Mapping with high
topographic similarity; high correlation between spaces.

Figure 3: Illustration of two spaces with different topographic similarities
(_toposims_). $\mathcal{O}$ and $\mathcal{M}$ represent embedding spaces for
the observations and messages, respectively. A high toposim means that
distances between any two points in the observation space correlates well with
distances between corresponding points in the message space.

Representation similarity analysis (Kriegeskorte et al., 2008; Luna et al.,
2020) takes a similar approach to quantifying compositionality but measures
the correlation in the feature space and agents’ internal representations. A
handful of other metrics fall under the umbrella of _disentanglement_ , where
components of the message specify single attributes and do so independent of
context. Such metrics include positional and bag-of-words disentanglement
(Chaabouni et al., 2020), context independence (Bogin et al., 2018), and
conflict count (Kuciński et al., 2020b). Tree reconstruction error takes a
deeper look at the compositionality of language by measuring how closely an
explicitly compositional model of semantics can approximate what the emergent
communication agents produce (Andreas, 2019).

In response to the amount of research into measuring compositionality, some
papers have provided deeper analyses of metrics of compositionality. Korbak et
al. (2020) provide a meta-analysis of the above compositionality metrics,
showing that while many are sensitive to basic types of compositionality, most
fail to recognize more sophisticated methods of composition. Kharitonov &
Baroni (2020b); Chaabouni et al. (2020) provide evidence against the claims
that standard measures of compositionality are also measuring the ability of
the language to generalize to describing novel objects.

##### Current state (other)

Generalizability, as in other areas of machine learning, generally refers to
the ability to perform well outside of the training conditions.
Generalizability is most often operationalized as agents successfully
describing objects previously unseen combinations of attributes (i.e.,
generalizing from training data to test data) (Korbak et al., 2019; Chaabouni
et al., 2020; Denamganaï & Walker, 2020a; Kharitonov & Baroni, 2020a; Resnick
et al., 2020; Perkins, 2021a). Apart from this type of generalization, other
work has looked at generalizing to new communication partners (Bullard et al.,
2021), generalizing over different environments (Guo et al., 2021; Mu &
Goodman, 2021), and generalizing across linguistic structures (e.g.,
disentangled syntax and semantics) (Baroni, 2020).

Yao et al. (2022) introduce an evaluation metric, that is, one which measures
the _overall_ quality of an emergent language using data-driven methods. The
metric equates the quality of an emergent language with the quality of machine
translation from the emergent language to human language. The underlying
intuition here is that the more human-like an emergent language is, the more
effective substituting it for human language will be in machine learning tasks
(i.e., using it as synthetic data, see Section 3.1).

##### Next steps

With regard to metrics of phenomena like compositionality, it is critical to
incorporate knowledge from linguistics as to how similar notions apply to
human language. For example, with compositionality, emergent communication
research often use to simple notions of compositionality, focusing individual
units of meaning combining arbitrarily to form composite meanings. On the
other hand, human language’s relationship with compositionality is far more
complicated, sometimes exhibiting it while sometimes being non-compositional
(e.g., idioms, irregular word forms, grammatical rules limiting “acceptable”
sentences). While this cross-disciplinary approach more difficult to
incorporate into the research process, it is critical to the long-term
trajectory of emergent communication research.

Evaluation metrics, on the other hand, are mostly absent in the emergent
communication literature despite their importance to other fields of machine
learning like reinforcement learning and natural language processing. Thus, it
would be fruitful to develop true evaluation metrics which can determine what
emergent languages are “best” or “most human language-like”. As these notions
are more abstract than “compositionality” or “generalizability”, there is more
theoretical groundwork that must go with the motivation of evaluation metrics
in addition to the engineering efforts in actually designing and implementing
them.

###  2.3 Theoretical models

##### Description

A theoretical model of emergent communication is a mathematical or formal
system which describes the behavior of an emergent communication system.
Generally speaking, a theoretical model will describe a relationship between
two or more variables in an emergent communication system. Theoretical models
are developed in conjunction with empirical work and represent a refinement
and systematization of the knowledge gained from these experiments. Most
importantly, their formal representation allows rigorously reasoning about the
behavior of a systems without needing to directly run experiments.

##### Applicability

Theoretical models benefit emergent communication research primarily in two
ways: they clarify research methods and can predict a system’s behavior in
compute-intensive situations. For research methods, using a theoretical model
to phrase a research question results in a hypothesis which is clear and
testable. As a result, the empirical evaluation has a clear relationship with
the assumptions and structure of the model, allowing subsequent research to
more easily build on previous work. In the absence of theoretical models and
their hypotheses, papers must often rely on qualitative hypotheses which are
difficult to empirically verify or result in merely pointing out “interesting”
observations from the experiments. For this reason, employing theoretical
models can move emergent communication research towards systematically
scientific investigation instead of less organized trial-and-error.

Second, theoretical models provide a way to predict the behavior of emergent
communication systems in situations where directly running the system is
computationally expensive. The applicability of theoretical models on this
front is discussed in the context of GPT-4 (OpenAI, 2023) and its scaling laws
where the extreme computational cost of training the model made it critical
that the designers could predict the behavior of the full-scale model ahead of
time. In particular, they fit a mathematical equation predicting the loss
based on computational input using smaller models. This gave the developers a
way to accurately predict the final loss of the full-scale model at a fraction
of the computational cost. As emergent communication environments get more
complex with more design choices, hyperparameters, and computational cost, it
will also be important to be able to predict the behavior of the system
without having to run the full environment in every situation.

Theoretical model EC game Time StepsLexicon Size Figure 4: Plots of lexicon
entropy ($y$-axis) versus time steps and lexicon size ($x$-axes) comparing a
theoretical model against empirical measurements on navigation game with
emergent communication (from Boldt & Mortensen (2022b)).999$\tau$ is the
Kendall correlation coefficient of the points. Theoretical models can help
predict the outcomes of emergent communication games much more efficiently
than simply running the environment while also providing a conceptual
understanding the environment’s behavior.

##### Current state

Only a handful of papers in the literature use theoretical models, and these
models are usually not employed in any subsequent papers. Khomtchouk &
Sudhakaran (2018) study the transition between two degenerate “phases” of
language: single-symbol systems and full one-to-one systems, with the synonymy
and ambiguity found in human language lying in the middle. This model is then
tested with a pair of simple reinforcement learning-based agents. Ren et al.
(2020) apply the iterated learning model (Smith et al., 2003) to deep
learning-based emergent communication; they use a formal probabilistic model
of an iterated learning algorithm to express hypotheses which are empirically
tested. Boldt & Mortensen (2022b) formulate a stochastic process which
describes the entropy of an emergent language’s lexicon based on a handful of
hyperparameters of the agent’s neural network; the predictions of this model
are also empirically tested in four simple emergent communication
environments. Rita et al. (2022b) analyze emergent communication environments
based on the Lewis signalling game by providing a mathematical decomposition
of the loss function. This decomposition explains the different overfitting
pressures hidden in the loss function; from this, they suggest measures to
counteract such pressures which result in more compositional emergent
communication.

Finally, the model presented in Resnick et al. (2020) is a good representative
of theoretical models in emergent communication and their attendant
difficulties. The model describes the relationship between the capacity of an
agent’s neural networks and the compositionality of the learned emergent
language: if the capacity is too low to capture the regularities in the
language (i.e., grammatical rules) the agents “underfit”, and if the capacity
is too high, the agents “overfit” by simply memorizing individual utterances
in the language. The model predicts the compositionality to be low in both the
under- and overfitting regimes and higher between them where the neural
network learns regularities without memorizing individual examples. The model
could then be formalized as follows

$\text{Capacity}(M_{X})\in\left[C_{L},C_{U}\right)\wedge\text{Capacity}(M_{Y})\not\in\left[C_{L},C_{U}\right)\Rightarrow\text{Comp}(M_{X})>\text{Comp}(M_{Y})$
(1)

where $M_{X}$ and $M_{Y}$ are the agents’ underlying models,
$\text{Capacity}(\cdot)$ quantifies a model’s capacity, $C_{L}$ and $C_{U}$
are the under- and overfitting thresholds respectively, and
$\text{Comp}(\cdot)$ quantifies the compositionality of the model’s emergent
language. 101010This is a summarization of the model which is more precise in
its original formulation. The particular formalization is not used in the
original paper and is instead derived from Boldt & Mortensen (2022c).

The precise formulation of the model results in a clear hypothesis based on
the predictions of the model, allowing the experiments to more directly test
the underlying principles of the model. The difficulties that persist, though,
are that the model’s formulation and its predictions still lack precision. In
the formulation, Resnick et al. (2020) do not fully articulate what
constitutes “capacity”; a notion of capacity though would be extremely
difficult if not impossible to formulate precisely for deep learning models.
In the predictions, the paper is only able to articulate general trends and
correlations rather than predicting exact values or distributions. These
issues, though, are representative of more general issues with theoretical
models in emergent communication the use of deep neural networks and
reinforcement learning make precision inherently difficult (although
approximation is not impossible as shown by the GPT-4 scaling case above).
Finally, despite the fact that the model in Resnick et al. (2020) addresses
compositionality, the most popular topic within emergent communication, it
does not see reuse subsequent papers.

##### Next steps

Theoretical models are difficult to apply deep learning-based emergent
communication since deep neural networks themselves are difficult to
formalize. Part of this difficulty is inherent while some of it stems from the
sparsity of formalization in the applications of deep neural networks. Thus,
an important next step would be to address these difficulties with
archetypical examples of how theoretical models can be applied to emergent
communication as well as recommendations for best practices, taking
inspiration from existing work on the theoretical foundations of deep learning
and complex systems.

Even given these difficulties, one-off instances of simple theoretical models
can be helpful in clarifying the contributions, hypotheses, and results of a
given paper. For example, instead of hypothesizing simply that changing $X$
will improve $Y$, it could be stated instead that there will be a positive
correlation between $x$ and $y$, where $x$ and $y$ are quantitative metrics of
$X$ and $Y$ respectively, and correlation is mathematically defined (e.g.,
Spearman’s rank correlation coefficient). This would facilitate experiments
which more clearly refute or a support a hypothesis and its underlying claims.

###  2.4 Tooling

##### Description

The central aim of tooling within emergent communication is to develop
apparatus that can be used to ease the process of implementing and running
experiments. Since emergent communication is under the broad category of
computer science, the experimental apparatus are most often programs, their
source code, and sometimes datasets. Although any codebase used for an
emergent communication experiment can be reused and repurposed by other
researchers for new experiments, codebases which are designed to be reused for
a broad range of experiments are the focus of this application.

##### Applicability

The most obvious benefit of shared and standardized tooling is that it saves
time for researchers as less time needs to be spent reimplementing the basic
features of emergent communication experiments. Furthermore, the incidence of
bugs decreases, implementation efforts can be spent improving existing
tooling, and comparison across papers is more reliable since more
implementation details will be the same. Special care must be taken, though,
that the implementation details do not lead to systematic biases in
experiments; emergent communication is especially susceptible to this concern
since it is difficult to distinguish between effects of structure of the
environment (abstractly speaking) and effects of implementation details.
Finally, well-designed and easy-to-use tooling is a significant help to
emergent communication researchers who do not have a strong software
development background. The task of putting ideas into code is much more
difficult for such researchers, and decreasing the amount of unnecessary
reimplementation can greatly improve their ability to contribute to the field
of emergent communication.

##### Current state

Tooling for emergent communication has a small degree of standardization,
although the high degree of variety in problems and approaches in the field
decrease the practicality of a one-size-fits-all framework. EGG (Emergence of
lanGuage in Games) (Kharitonov et al., 2019) is the most widely used framework
for deep learning-based emergent communication; it provides a simple Python
programming interface for some of the most common emergent communication
games, agent architectures, and metrics. Papers which implement new games
using EGG further expand the range of games and metrics which are easily
accessible through the framework including: Chaabouni et al. (2019b); Dessì et
al. (2019); Chaabouni et al. (2020); Auersperger & Pecina (2022); Kharitonov
et al. (2020b). ReferentialGym (Denamganaï & Walker, 2020b) is similar to EGG
in scope, although it has seen less reuse within the literature. Other tooling
may target more specific aspects of experiments in emergent communication. For
example, TexRel (Perkins, 2021b) is a synthetic dataset designed specifically
for use in emergent communication games; in this case, data (images) are
constructed such that _compositional_ language could aptly describe them.
Additionally, Ikram et al. (2021) introduce HexaJungle, a suite of
environments for studying emergent communication. For papers which explore
beyond the typical environments (which is a significant portion), it is common
to implement the emergent communication game and agents from scratch using
more general purpose tools like PyTorch (Paszke et al., 2019) (Evtimova et
al., 2017; Mu & Goodman, 2021; Noukhovitch et al., 2021).

##### Next steps

The next steps for tooling in emergent communication largely depends on what
tasks, problems, and methods receive the most attention going forward. If the
field continues to study similar environments, EGG could continue to support
such work, but if radically new environments or experimental paradigms appear,
current tooling might prove insufficient. In part, this is due to an inherent
trade-off between the flexibility of a framework and its convenience; while
emergent communication is rapidly changing, the required flexibility often
does not provide much convenience, but as the field focuses on fewer problems,
frameworks could play a greater role. A possible middle way between these two
issues would be developing an interface (in the sense of object-oriented
programming) for emergent communication environments similar to OpenAI Gym
(Brockman et al., 2016), which can provide some standardization and
interoperability while not impeding novel environments and implementations.

##  3 Task-Driven Applications

The task-driven applications of emergent communication center around fields of
engineering such as machine learning, natural language processing, and multi-
agent systems, and typically involve solving a well-defined, practical
problems. These applications have the most immediate impacts and, as such,
offer some of the most convincing motivations for developing emergent
communication techniques in the short term. The primary challenges in this
area come from competing against more established methods in deep learning
which are continually advancing through larger and larger scales of data and
compute.

###  3.1 Synthetic language data

##### Description

In the context of deep learning, synthetic data refers to data which is
generated with a computer program; this is in contrast to “real” or “natural”
data which is collected from an actual system being studied. For example, the
language we find in books, conversations, speeches, etc. would all be real, in
this sense, whereas a corpus of sentences generated by sampling from a
probabilistic context-free grammar would be synthetic. For example, within
NLP, synthetic data can be used for transfer learning (Papadimitriou &
Jurafsky, 2020; Mirzaee & Kordjamshidi, 2022) and model probing (Lake &
Baroni, 2018; Zhang et al., 2022). Synthetic data has a number of advantages
when applied to deep learning; this includes: controllability, availability in
arbitrary quantities, availability in low-resource domains (e.g., endangered
languages, multi-modal settings), alleviating concerns about bias, and
alleviating privacy concerns (since data is not collected from humans, e.g.,
through surveillance). Although synthetic data finds niche uses alongside real
data in deep learning and NLP, it fails to have widespread applicability
because it often does not capture the plethora of nuances and irregularities
that appear in real data, that is, the “long tail” of the real data
distribution. In natural language, this can manifest as unnatural but valid
syntactic structures, uncommon senses of words, idioms, and wordplay.

We consider all kinds of pretraining, data augmentation, analyses, and
evaluation of deep learning models with emergent communication as part of this
application even if it does not involve generating synthetic datasets _per
se_. For example, you might use a trained emergent communication agent itself
to pretrain or evaluate a model instead of generating an intermediate dataset.

##### Applicability

Emergent communication could serve as a way to generate synthetic language
data which more closely mimics the natural variation found in human language.
The distribution of patterns within natural language has a “long tail” insofar
as a large proportion of the total mass comprises a large number of infrequent
patterns, making it very difficult for something like synthetic data generated
by handcrafted programs to sufficiently replicate the distribution (Naik,
2022). This is illustrated by the history of NLP: handcrafted expert systems
have been surpassed by learning-based method which can scalably leverage
computing power to mine patterns from increasingly large quantities of data.
Emergent communication, rather than mining patterns directly from data, seeks
to uncover linguistic and behavioral patterns which are latent in the
communicative pressures of embodied multi-agent environments. Work such as
Artetxe et al. (2020) demonstrates that deep neural networks do learn latent,
language-agnostic patterns from their training data; this suggests that even
if an emergent language does not have a one-to-one correspondence with some
particular human language, having underlying structural similarities with
human language would be sufficient to still be useful.

##### Current state

Work towards using emergent communication to generate synthetic data has been
at the proof-of-concept level. The papers in our survey (discussed below)
showed that emergent communication could indeed improve the performance of
neural NLP models when used for pretraining in very low-resource settings.
That being said, experiments only cover a narrow selection of datasets/tasks
and do not rigorously compare against alternative methods (e.g., traditional
synthetic data, cross-lingual transfer). As a result, is difficult to gauge
the practical impact of the proposed methods.

Li et al. (2020) pretrain encoder-decoder few-shot machine translation models
with an emergent communication signalling game; in addition to finding
improvements in very low-resource settings, the experiments showed that the
task success rate in the emergent communication game was well-correlated with
the downstream BLEU score. Downey et al. (2022) also tackle machine
translation, but instead use an emergent communication game to fine tune a
multi-modal model for unsupervised machine translation, finding that emergent
communication is more effective than the back-translation baseline. Yao et al.
(2022) take a slightly different approach by using the emergent communication
game only to generate a synthetic corpus (instead of training the models
directly); this corpus is then used to pretrain models for language modeling
and image captioning tasks. The experiments compare emergent language corpora
against two baselines: Spanish and a synthetic dataset generate by sampling
delimiters from a Zipfian distribution to create a hierarchical language with
similar structural biases to human language (e.g., {<()>[]()}). For the lowest
data regimes, pretraining the model on emergent language corpora reliably
outperforms models pretrained on the baseline datasets. Finally, Mu et al.
(2023) use emergent communication to pretrain an instruction-following
embodied control model (e.g., for controlling a robotic arm); the experiments
showed that not only does the proposed method outperform the baseline models
but also that the emergent language is more effective as training data than
pre-trained, static representations derived from video demonstrations.

##### Next steps

The first direction is thoroughly investigating the different ways emergent
communication can be used for generating synthetic data. Li et al. (2020)
(using emergent communication agent models directly downstream) and Yao et al.
(2022) (using emergent language corpora for pretraining downstream models)
take different approaches to the same task of pretraining downstream NLP
models. These approaches have different relative merits (e.g., making better
use of training data versus decoupling agent architecture from downstream
architecture, respectively), and there are likely more ways to approach the
same problem with emergent communication. Thus, next steps would consist of
finding other promising methods of harnessing emergent communication for model
pretraining and comparing these approaches on a common ground. Determining
which of the approaches is best is critical to giving emergent communication
the best chance of surpassing more traditional methods model pretraining and
generating synthetic data.

The second direction which can be pursed after or in parallel to the first is
rigorously comparing emergent communication for pretraining neural NLP models
with more established techniques like cross-lingual transfer and traditional
synthetic data (Artetxe et al., 2020). First and foremost, this helps to
establish whether or not emergent language data can truly surpass what is
already present in the field. In particular, comparison against cross-lingual
transfer should highlight how emergent language data is more available, that
is, it can be attained in higher quantities with more relevance to the target
language than cross-lingual data. Comparison against traditional synthetic
data could tease out exactly what properties of emergent communication make it
more effective in downstream applications. For example, emergent communication
could be compared against increasingly complex synthetic languages: balanced
parentheses, context-free grammars, then full-scale grammars (e.g., head-
driven phrase structure grammar (Pollard & Sag, 1994)).

Both directions would entail developing a sort of benchmark for testing the
effectiveness of pretraining methods. This would require not only finding
suitable data sources and evaluation metrics, as usual, but also determining
how to make the variety of methods for pretraining comparable. For example,
emergent communication is more computationally expensive than traditional
synthetic data and standard NLP pretraining methods, yet it could surpass
synthetic data in quality and real data in low-resource settings. Therefore
the benchmark would have to take into account data and computational
requirements in addition to raw performance.

###  3.2 Multi-agent communication

##### Description

The area of multi-agent communication is concerned with autonomous (computer)
agents coordinating their actions through the use of a communication protocol.
Most prototypically, this would apply to a team of autonomous robots working
together but could also include situations like self-driving cars on the road
(illustrated in Figure 5) or IoT devices on a local area network. The two
typical approaches to developing multi-agent communication protocols are
handcrafting them or learning them like a latent variable between agents.
Handcrafted protocols (e.g., DHCP for network configuration) are typically
well-suited for specific tasks but are also require significant expert design,
which hinders much potential for open-domain or general purpose communication.
Automatically learned continuous protocols (i.e., messages are learned
continuous vectors) solve some of these issues but raise new issues related to
deep, learned representations such as low interpretability. This task is
distinct from autonomous agents communicating directly with humans, which we
discuss in Section 3.3.

(a) Complex scenario with various kinds of agents.

(b) Pixel-based input to approximate real-world diversity in observations.

Figure 5: Self-driving vehicles in complex traffic situations are an important
application of multi-agent communication. Diversity in both scenarios as well
as the observations themselves indicate that open-ended communication systems
could be more appropriate than handcrafted protocols where all scenarios are
anticipated ahead of time. Screenshots from documentation of MetaDrive (Li et
al., 2023) (Apache-2.0 license).

##### Applicability

Emergent communication addresses these issues in three main ways. First,
emergent communication is scalable to more general-purpose tasks since it is
developed by computational processes directly from the functional pressures of
the task it is applied to. Second, it is more interpretable insofar as it
resembles the structure of human language, for example, using discrete symbols
in its communication channel or having a hierarchical syntactic structure (cf.
continuous vectors which do not resemble human language and require
mathematical transformation to be analyzed). Finally, human language is the
gold standard for communication protocols insofar as it can apply to
previously unseen situations and is robust to noise and other hindering
factors. Thus, developing communication protocols which deliberately mimic the
structural properties of human language could be a way to better attain these
desirable functional properties.

For example, the following design elements of an emergent communication system
could contribute to recreating some of the above desirable properties of an
emergent language. To encourage general purpose language, we can start with an
open world, open-ended environment (e.g., Minecraft) and/or one with many
distinct situations (e.g., Starcraft, Dota 2). Furthermore, tasks which have
adversarial components can especially elicit a diversity of situations since
one team of agents is constantly trying innovate to outcompete the other.
Towards interpretability, the agents could be constrained to communicate only
with discrete symbols at human-scales (e.g., modest vocabulary size and
message length). Finally, elements like communication channel noise or
constantly cycling out agents in the population can induce a more robust
communication protocol since agents cannot as easily overfit to each other.

##### Current state

Work on developing multi-agent communication protocols has experimented with a
handful of environments and scenarios but has not established any one task as
being definitively helped by emergent communication. Many of the explored
environments are a variation on navigation (Mul et al., 2019; Li et al., 2022;
Masquil et al., 2022) or the signalling game (Bullard et al., 2021; Cope &
Schoots, 2021; Wang et al., 2022; Tucker et al., 2021), although some include
more abstract environments like a coalition-based voting game (Li et al.,
2022) or semantic communication (Thomas & Saad, 2022). Emergent communication
for multi-agent communication has been compared against competing methods,
that is, handcrafted protocols (Gupta et al., 2020; Chen et al., 2022) and
learned continuous communication (Li et al., 2022; Wang et al., 2022).
Although, these comparisons use the competing methods more as “baseline points
of reference” rather than comparing them “head-to-head”, where both methods
are presented in their strongest forms so as to show the real-world
superiority of emergent language-based communication. In most cases,
increasing the performance of the multi-agent team is the primary interest of
the experiments; additionally, papers have also looked at emergent
communication’s robustness to corruption and noise (Cope & Schoots, 2021; Wang
et al., 2022) as well as the potential for communicating with partners not
seen during training (Bullard et al., 2021; Cope & Schoots, 2021).

##### Next steps

The first direction of future work on emergent communication for multi-agent
communication is to find a niche for emergent communication, that is,
presenting a particular task where, in realistic conditions, emergent
communication surpasses state of the art non-emergent approaches. Although
emergent communication has intuitive advantages (discussed above in
“Applicability”), it has yet to be shown in a real-world task. This is a
significantly more difficult task than what most of the current literature
accomplishes: namely demonstrating a on small scale that multi-agent
communication is possible with emergent communication techniques as a proof of
concept. Based on the particular advantages of emergent communication, such a
task will likely have to be open-domain or demand continual adaptation,
rendering hand-crafted protocols impractical, while also needing some element
of interpretability, demonstrating an advantage over learned continuous
communication. This is a formidable task as presenting effective definitions
of “open-domain” and “interpretable” require formalizing rather abstract
notions.

In conjunction with this first direction, it will also be necessary to
empirically verify the intuitions that (1) emergent communication is more
interpretable than continuous communication, and (2) emergent communication’s
structural similarities to human language confer some actual functional
benefit beyond continuous or unconstrained communication. If these intuitions
are well-founded, then it will greatly expand the potential applications of
emergent communication in multi-agent systems.

###  3.3 Interacting with humans

##### Description

A perennial goal of computer systems has been more naturally interacting and
communicating with humans. This is an incredibly difficult task due to the
complexities of human communication ranging from nuanced syntax and semantics
to pragmatics and conversational dynamics. While deep learning methods have
had good success learning syntax and decent success learning semantics,
proficiency at the level of pragmatics is not yet present because these higher
levels of language and communication tend to be more difficult to learn from
purely from text through a language modeling objective. This is demonstrated
in Ouyang et al. (2022) by the fact that an InstructGPT model outperforms a
GPT-3 model $100{\times}$ larger when it comes to following a human’s
instructions (as evaluated by humans). They observe from this that the
language modeling objective alone is misaligned with the objective of “follow
the user’s instructions helpfully and safely”; for example, truthfulness is
one dimension that is drastically increased by training on human feedback
(Ouyang et al., 2022). Even with extensive training with human feedback,
models like ChatGPT still significantly diverge from humans when it comes to
pragmatics and communication strategies (Qiu et al., 2023; Guo et al., 2023).
Thus, despite large language models’ fluency, they do not naturally capture
critical aspects of interacting with humans, and current methods of addressing
it entail relying directly on human supervision (Ouyang et al., 2022).

This application primarily refers to methods of interactively communicating in
tasks like dialogue or human-robot collaboration. We distinguish this from
creating explainable machine learning models which we address in Section 3.4.

##### Applicability

The central argument for using emergent communication to better communicate
with humans comes from the fact that an emergent communication agents
naturally develop competency with a wide range of linguistic phenomena. The
hypothesis here is that the same functional pressures that drive the pragmatic
and social aspects of human language could be replicated by sufficiently rich
and embodied emergent communication environments. Thus, the emergent
communication agents could not only develop the syntax and semantics of the
language but also pragmatic elements in response to the environmental and
social pressures. In fact, Bisk et al. (2020) argue that embodiment and
interaction, beyond simply modeling static corpora, are necessary for learning
to use the full depth of language. Emergent communication, then, could be a
more compute-driven (and less human-feedback intensive) way of imbuing machine
learning models with a full range of linguistic competency that is necessary
for seamlessly interacting with humans.

##### Current state

Communicating with humans is an oft-cited potential application of emergent
communication techniques, although few papers have directly experimented with
it. The papers we found in the survey were proof-of-concept tasks which
demonstrated some possible methods for emergent communication agents
interacting with humans. One of the characteristic design choices of each
paper is deciding how to structure the communication channel between the human
and agents.

(a) User interface for interpreting the meaning of an emergent communication
method from a visualization of the message’s embedding (Tucker et al., 2021).

(b) Example of a “message” from a sketch-based game (from documentation of
Mihai & Hare (2021a)’s code).

Figure 6: Two examples of agent-to-human emergent communication interfaces.

For the direction of human-to-agent communication, Tucker et al. (2021) map
natural language to a joint embedding space with the emergent language, making
the embedded natural language understandable to the agents. Li et al. (2022)
have humans select embeddings directly from a labelled visualization of the
embeddings (i.e., t-SNE) of emergent language messages. For the direction of
agent-to-human communication, Tucker et al. (2021) visualize the embedding of
agent messages in a labelled embedding space, allowing a human to determine
which cluster of messages an unlabelled message belongs to (shown in Figure
6(a)). Mihai & Hare (2021a) show the human directly with a “message” (i.e.,
sketch) in a sketch-based signalling game (shown in Figure 6(b)). Apart from
direct human-agent interaction, Tucker et al. (2022a) demonstrate machine
translation-based approach where human and emergent languages are aligned
through an image captioning task.

##### Next steps

The first direction for using emergent communication to augment human-computer
interaction is to determine the most the natural and scalable methods and
modalities for human and computers to communicate. The existing literature
uses a handful of methods some of which are either unnatural and not scalable
to more complex communication (e.g., interacting with concept/word
embeddings). Work on non-emergent human-computer interaction can inform
emergent communication research not only on methods of communication but also
concerning what environments would have the potential for complex
communication while still being simple enough to work with. For example,
Narayan-Chen et al. (2019) present a collaborative building game in a
Minecraft environment which could satisfy these criteria.

The second direction for this application is empirically demonstrating the
intuitive advantages of emergent communication over more established methods
for human-computer interactions. The pragmatics of interacting with humans is
one of the areas with the most potential because pragmatics are inherently
flexible, tied to extra-linguistic knowledge, and are more difficult to
formalize than, say, syntax or semantics. Nevertheless, emergent communication
could help in the more difficult regions of syntax and semantics, such as
disambiguating utterances which rely on contextual knowledge or common sense
reasoning.

###  3.4 Explainable machine learning models

##### Description

Explainable machine learning models are those which can communicate to humans
the reasons or factors behind a certain their decision. Such model are a
response to deep, black-box neural models which may be able to make accurate
decisions but often for opaque or seemingly arbitrary reasons. Instead it is
desirable for explanations to be: (1) causally related to actual decision that
is made (i.e., not a _post hoc_ rationalization); (2) expressed natural
language, which is one of the most effective ways to convey ideas to humans;
and (3) not impose a significant negative impact on the performance of the
model.

Two paradigms of explainable machine learning models illustrate solutions only
satisfying some of the criteria. The first paradigm is using language
generation models to generate explanations based on the hidden states of a
model; while this permits the use of deep neural models, the explanations are
decoupled from the actual decision since the explanation is superfluous with
respect to the actual decision. The second paradigm is using explicit,
interpretable steps in reasoning to the prediction (e.g., decision trees,
knowledge graphs); although these explanations are now causally efficacious
with respect to the prediction, it restricts the complexity of model that can
be used to make the prediction. While the explanations these models generate
are intrinsically related to the decisions made (e.g., the weights of a
regression both explain the decision and cause it), they restrict the
complexity of the model, and hence, can hamper overall performance.

_Explainable machine learning models_ , in some sense, is a subclass of
Interacting with humans (Section 3.3); here the interaction is always focused
on a machine learning model communicating accurate and interpretable
explanations for its decision or behavior.

##### Applicability

Emergent communication takes a radical approach to both the causal efficacy
and the natural language aspect of explainable models. To illustrate this, we
can describe a “deliberative ensemble of emergent communication agents”. Such
an ensemble would be posed a semi-adversarial game where first each member of
the ensemble would generate an output for a given input. After this, the
ensemble members would communicate in the emergent language to try to convince
the other members of the particular output before aggregating the members’
revised decisions. Given that emergent language is designed to resemble human
language, the representation mismatch between natural language and the
emergent language discourse is far less than natural language and the
activations of a monolithic neural network. Furthermore, since the
deliberation and communication among agents is critical in the final decision
of the ensemble, the explanation has a direct causal link to the decision.

##### Current state

Using emergent communication for creating explainable machine learning models
has only seen proof-of-concept exploration in one series of papers. Namely,
Santamaria-Pang et al. (2020); Chowdhury et al. (2020a; b; c) implement and
experiment with a medical image classification model which, internally, is a
Lewis signalling game (Lewis, 1969). This means that the internal
representations are themselves the discrete messages of an emergent language.
Messages-as-internal representations, here, are intended to be a more natural
modality for human working with the system than, for example, the activations
of intermediate layers in the neural network.

##### Next steps

The first direction for using emergent communication for explainable machine
learning models is exploring methods of generating explanations beyond the
signalling game that we see in the current literature. The signalling game,
while providing potentially interpretable messages, does not effectively
exhibit the multi-step reasoning which (1) is most suited to the complex
decisions which we would want explained, (2) is how humans generally explain
themselves, and (3) is where emergent communication has the greatest potential
to surpass more established methods. Such games or environments might
incorporate incentives for agents to collaborate and reason sequentially using
the emergent language. This reasoning process would then double as the basis
for the decision and the explanation of the decision.

The second direction is incorporating state-of-the-art models into the
emergent communication systems. This application, more so than others,
requires that the emergent communication-based model perform comparably on
downstream tasks to more established explainable machine learning models; even
if the emergent communication-based models are highly explainable, they are of
little practical use if they are not comparable in performance to traditional
approaches. Given the size of current state-of-the-art models and inherent
difficulty of training emergent communication models, this incorporation, in
the near term, would likely be limited to leveraging pre-trained models which
could be, at most, finetuned.

##  4 Knowledge-Driven Applications

The knowledge-driven applications of emergent communication center around the
scientific fields of linguistics and cognitive science and typically concern
gaining a deeper understanding of phenomena in the natural world. These
applications have tend to have more remote impacts than the task-driven
applications, but they also present the opportunity to gain novel insights
into how humans think and use language. The primary challenges in this area
come from creating emergent communication which is realistic enough to
legitimately provide insight in areas where there are gaps left by more
traditional techniques in linguistics and cognitive science. The first
subsection below (Section 4.1) provides a summary of common themes in the
“Description” and “Applicability” subsections throughout knowledge-driven
applications (i.e., it is not itself an application).

###  4.1 General paradigm of knowledge-driven applications

##### Description

Some of the most persistent debates in linguistics are about the degree to
which language and its characteristics are the product of very specific
biology (the “Chomskyan” nativist position that dominated North American
linguistics in the second half of the twentieth century) or can be derived
from very general mechanisms of learning (the behaviorist position that
dominated North American linguistics in the first half of the twentieth
century). This conflict reflects a broader debate within the social and
behavioral sciences about the relative importance of “nature” (the inductive
biases of the human brain) and “nuture” (operant conditioning from parents,
caregivers, and other aspects of the environment) in the cognitive development
of human children. Such debates are difficult to resolve because of limited
access to the necessary data: the ingredients of language (nature and nuture)
are largely fixed, meaning we cannot (ethically) vary them in order to
determine their effects on language. This is to say, the relevant data in
these debates come largely from observation and only extremely limited
experimentation. The lack of true experimentation hinders the type of
scientific investigation which would yield more definitive answers to these
questions.

##### Applicability

Emergent communication can address these unsolved problems by serving as a
proxy for human language whose ingredients can be manipulated and experimented
with. Emergent communication makes a suitable proxy because (1) it aims at
being a faithful reconstruction of human language, and (2) this reconstruction
is a reflection of its ingredients. For example, we can see the “nature vs.
nurture” distinction paralleled in the distinction between the systems inside
of an agent and the interaction that takes place with other agents.

Deep learning-based emergent communication is uniquely poised to serve as a
proxy for human language for two reasons. First, deep learning methods are by
far the closest methods to replicating human proficiency in language (as well
as vision, planning, and so on). Hence, it would seem a model class of
comparable power is necessary to support the emergence of a language with
enough complexity to be useful for the most relevant linguistic problems.
Second, deep neural networks also introduce minimal inductive bias when
compared with traditional simulations and mathematical models. The behaviorist
or “nurture” position can only be validated if language learning can take
place without language-specific inductive biases and this is only possible in
a context in which learning according to very general principles is possible,
so deep learning is a natural fit for testing hypotheses about the necessity
of language-specific learning mechanisms.

###  4.2 Language, cognition, and perception

##### Description

This goal refers to the two-way relationship between language and cognitive
(and perceptive) processes in the human brain: how language is shaped by the
cognitive capacities of humans and what goes on in the brain to enable the use
of language. By extension, this also includes behavior which proceeds from
cognitive phenomena of interest (e.g., adjusting communication strategies
based on a theory of mind). Aside from not being able to experimentally modify
the brain, a major barrier in studying cognition is being able to merely
_observe_ the brain.

The primary way of studying language and cognition has been through laboratory
experiments with humans. While we do have easy access to humans using
language, the observation of the actual cognitive processes we are interested
has limitations in both its direct and indirect forms. Direct observation
includes using apparatus like an EEG, MEG, or fMRI; its primary disadvantages
are that it requires specialized instruments, often cannot be done _in situ_
and is still limited with what it can observe. Indirect observation includes
methods which infer cognitive processes from external observations; for
example, we might infer a limit to working memory by seeing how many digits in
a long number a person can recall. The primary restrictions with indirect
methods is that they, too, are very limited in what they can observe.

Some approaches to simulation for this application investigate the similarity
of language models to humans in the cognitive domain (Schrimpf et al., 2020;
Misra et al., 2021; Mahowald et al., 2023). These neural networks, though, are
typically trained in a standard supervised or self-supervised manner (i.e.,
not the embodied reinforcement learning of emergent communication). Even if
the model is trained with multi-modal data, the relationship between the
modalities is more rigid insofar as it is restricted _a priori_ by the way the
model is optimized; this limits the ability to draw conclusions about human
linguistic behavior where relationship between modalities is flexible and
dynamic.

##### Applicability

(a) Visualization of fMRI scans of the human brain in response to visual
stimuli (Bracci et al., 2023).

(b) Visualization of image classification CNN layers (Olah et al., 2017).

Figure 7: Measurements and visualizations of artificial neural networks are
easier to make and far more flexible compared to biological networks in the
human brain. This makes ANNs an attractive proxy for studying the human brain.

Observing neural networks is easier that observing the results of human-
subject experiments. This is because the state and processes of artificial
neural networks are completely accessible, even if they are not always easy to
interpret. Furthermore, any individual aspect of an artificial neural network
can be manipulated, which allows for a far higher granularity in
experimentation than human subjects. Compared to using language models,
emergent communication agents have a more natural integration of language
capabilities with other capabilities such as perception or interpersonal
communication goals. This is due to the automatically learned neural-to-neural
interface between between language, cognition, and perception, allowing the
resulting use of language to be shaped by embodiment and pressures for useful
communication.

##### Current state

The current literature in this area focuses on observing high-level principles
from cognitive science and perception in the context of emergent communication
systems. While these abstract facts do relate to cognitive science, they are
more directly aimed at improving emergent communication techniques themselves
(i.e., like an _internal_ goal). Work directly applying emergent
communication-trained models to particular questions within cognitive science
(along the lines of Misra et al. (2021)) is largely absent.

The subtopic with the most attention in this application is the relationship
between emergent communication and the agents’ perception of the environment.
Bouchacourt & Baroni (2018) establish a simple but important point regarding
perception: neural-network based agents may successfully communicate with
degenerate perceptual strategies. Namely, they show how agents which learn to
play an image discrimination game with natural images are just as successful
when playing with random noise images, demonstrating that we cannot simply
assume that agents will learn intuitive or interpretable perceptual
representations without further investigation.111111This is closely related,
both technically and methodologically, to adversarial inputs in computer
vision research. Nevertheless, Dessì et al. (2021) counter this pessimism by
demonstrating that it is still possible for emergent communication agents to
develop interpretable visual representations on their own.

Choi et al. (2018); Portelance et al. (2021) study how the balance of visual
attributes in training data directly influences what attributes are actually
perceived. Feng et al. (2023) look specifically at _relations_ between visual
elements in a referential game. More generally, Lazaridou et al. (2018); Ohmer
et al. (2021b; a) study how the emergent communication is sensitive, in
general, to the perception of the environment. While most papers address
_visual_ perception, Khazar Khorrami (2019) looks at the emergent perception
of units of sound.

Deeper than perception, some work studies the agents’ internal representations
themselves. Sabathiel et al. (2022) look at how agents can represent numbers
to themselves by interacting with their environment (e.g., an abacus).
Santamaría-Pang et al. (2019) compare representations learned with supervised
methods (e.g., a convolutional neural network trained on image classification)
with those learned with self-supervised learning; supervised learning yields
better representations, generally, but self-supervised learning can be
augmented to approach the same performance. Garcia et al. (2022) discuss how a
mismatch in internal representation severely reduces the effectiveness of
communication.

Finally, a handful of papers have addressed cognitive strategies themselves
and specifically how human-inspired inductive biases can be beneficial both
for task success and for learning intuitive representations. Todo & Yamamura
(2020) find that agents restricting their own learning process lead to
languages with more interpretable structure; specifically, agents would
discard training examples which diverged more than certain threshold from
their own representations. Yuan et al. (2020); Piazza & Behzadan (2023)
encourage agents to develop a theory of mind by explicitly modeling the
internal states of other agents. This leads to more effective communication by
introducing pragmatics into the emergent communication since agents can
explicitly infer meaning from the communicative context. Masquil et al. (2022)
propose adding intrinsic motivations to agents to improve communication.
Finally, Cowen-Rivers & Naradowsky (2020) explore the use of world models (Ha
& Schmidhuber, 2018) to improve agents’ ability to handle environments with
longer episodes.

##### Next steps

The next steps for this area of emergent communication are to bring the
research which already explores abstract principles of cognition in emergent
communication closer to the more concrete questions already present in
cognitive science. This would entail using emergent communication techniques
in the same vein as Misra et al. (2021) and the other papers mentioned in the
“Description” section. In particular, it would be especially important to
identify the differences between traditional language models and emergent
communication agents in terms of their cognitive realism. This would include
both ways in which language models should be limited (e.g., language models
having super-human recall) as well as ways in which they need to improve
(e.g., discourse coherence, factuality). Incorporating cognitive science will
better illuminate where emergent communication techniques diverge from human
cognition and behavior and how that might influence the resulting emergent
communication.

###  4.3 Origin of language

##### Description

The origin of human language, as a task, comprises studying the environment
and processes under which human language, as we recognize it today, emerged
from pre-linguistic communication (e.g., methods animals use to communicate,
see Figure 8). In particular, one of the biggest questions surrounding the
origin of human language is whether it occurs gradually or through saltations
(discussed in LaCroix (2019)). The gradualist position holds that there was no
clear boundary or and no clear discontinuities between pre-linguistic
communication and true human language while the saltationist position holds
that, at some point, pre-linguistic communication underwent a sudden
transition into human language. Addressing this particular question is major
step in determining the nature of the processes explaining the origin of human
language.

Figure 8: Illustration of the continuum of pre-linguistic communication from
simple, low-bandwidth communication (e.g., ants leaving pheromone trails) to
more complex, high-bandwidth systems (e.g., the vocalizations of Rhesus
monkeys) (examples and figure taken from Grupen et al. (2020a)). Pre-
linguistic communication systems, such as these, are an important component of
studying the origin of human language.

Since language was originally only spoken, there are no direct data which
describe what happened when it evolved. Thus, any data for research come from
inferential data from animal communication, and contemporary examples of
language invention (e.g., creolization, Nicaraguan Sign Language). These are
relatively sparse, leaving the origin of language very difficult to study. As
a result, simulation is, in a way, the closest source of data to direct
observation. Yet critical factors in the origin of language include complex
non-linguistic elements such as perception, internal representation, and
social dynamics which traditional simulations have difficulty representing.

##### Applicability

Simulation is a natural way to address processes, such as the origination of
language, for which we have no (or limited) direct observations. Simulations
permit not only observing these processing but counterfactually experimenting
with them as well (e.g., answering “If I change variable $X$, how does $Y$
respond?”). Such experiments are necessary for scientifically distinguishing
causation from mere correlation. Yet, the dependence of the origin of language
on non-linguistic factors like perception, internal representations, and
social dynamics indicates a significant need for simulations which integrate
learning methods with a high capacity and flexibility, that is, deep neural
networks. Furthermore, learning these linguistic and non-linguistic skill
jointly (as opposed to, for example, using a pre-trained vision network) is
also an important point of realism which emergent communication provides as it
mirrors the fact that humans learn language and other cognitive skills
jointly. Additionally, using neural networks allows the simulation to reflect
the evolutionary pressures in the environment instead of the stipulations of a
handcrafted mathematical model.

##### Current state

Work on language evolution and change comprises a few empirical papers which
have used small-scale, simple environments to test specific hypotheses as well
as a few position papers. The empirical papers typically use environments and
tasks from prior work with the added element of transmission of language from
generation to generation. For example, Grupen et al. (2021) look specifically
at pre-linguistic communication (e.g., between animals) with emergent
communication techniques as a foundation for the emergence of fully linguistic
communication. Li & Bowling (2019); Ren et al. (2020) test the effects of
_iterated learning_ in emergent communication environments. Iterated learning
is a framework introduced by Smith et al. (2003) as a way to reason about and
explain the origin of compositionality (among other things) in human language
from an evolutionary perspective on language (Kirby & Hurford, 2002; Kirby et
al., 2008). The core feature of iterated learning is that when language users
transmit only a subset of the language to language learners, the learners have
to generalize what they have heard in order to infer the rest of the language,
leading to greater systematicity and compositionality over generations.

The position papers on this topic all specifically incorporate relevant work
from the linguistics side of language evolution and try to square it with the
contemporary approaches of emergent communication. LaCroix (2019) compares the
relative merits of gradualist and saltationist approaches to the origin of
language and what bearing they have on emergent communication research,
specifically arguing that the focus on compositionality might not align with
gradualism. Moulin-Frier & Oudeyer (2020) highlight the opportunities and
challenges of using recent advancements in multi-agent reinforcement learning
for studying the origin of language. Galke et al. (2022) specifically identify
the elements of current emergent communication research that must change in
order to better apply to linguistically-grounded study of the origin of
language.

##### Next steps

Achieving realism in emergent communication-based simulations of the origin of
language must focus on closing the gap between the two data points we do
actually possess: animal communication and behavior (pre-origin of language)
and contemporary human language (post-origin). Thus, the pre-origin side of
this entails aligning emergent communication settings with what we can
currently observe in the more sophisticated varieties of animal communication,
along the lines of what Grupen et al. (2021) study. Subsequently, changes to
the setting would be made to elicit more sophisticated forms of communication
which would ideally result in communication bearing the traits of human
language (i.e., rederivation as described in Section 2.1). Since the origin of
language depends heavily on the aforementioned non-linguistic concepts,
simulations will have to take into account the relevant literature in
cognitive science and behavioral psychology.

Additionally, empirical implementations of the principled, interdisciplinary
recommendations of the position papers (LaCroix, 2019; Moulin-Frier & Oudeyer,
2020; Galke et al., 2022) also present concrete opportunities for quickly
advancing emergent communication’s relevance to studying the origin of
language.

###  4.4 Language change

##### Description

Languages are perpetually changing, sometimes above and sometimes below the
level of conscious awareness. Language change refers to the processes which
govern how language changes and develops over time in human populations. In a
groundbreaking paper in language change, Weinreich, Labov, and Herzog
identified five problems regarding how languages change over time Weinreich et
al. (1968):

constraints

What constrains the transition of a language from a state $s_{t-1}$ to a
successor state $s_{t}$? In particular, are there impossible languages that no
change could produce?

transition

What intervening stages must exist between states $s_{t-1}$ and $s_{t}$? For
example, do the two language varieties coexist for a time?

embedding

How are the observed changes embedded in the matrix of linguistic and
extralinguistic concomitants of the forms in question? What other changes co-
occur with the change non-accidentally?

evaluation

How do members of the language community subjectively evaluate the change that
is underway or has occurred?

actuation

Why does a particular change occur at a particular point in time and space?

While human laboratory experiments have been useful in addressing some of
these problems (Roberts, 2017), as have field studies and other social-
scientific methodologies, emergent communication simulations provide an
unprecedented means of addressing all of these problems except _evaluation_.

##### Applicability

Emergent languages in multi-agent simulations change over time. If they did
not—in some respect—change, they would never develop language-like properties
in the first place. Thus we can ask if they reach stable equilibria and, if
so, where and why do changes occur, if at all. In answering this question,
emergent communication simulations can address the _actuation problem_ (one of
the most difficult problems in language change). These simulations allow us to
dissect the relationships between language changes and changes in the “social”
and “physical” environment as well, addressing the _embedding problem_. But
because emergent communication simulations give us a kind of omniscience, they
also allow us to characterize the stages between stable equilibria, providing
a window onto the _transition problem_. Finally, because emergent
communication researchers are free to add and remove constraints on possible
languages at will, such simulations allow us to address questions about
whether human-like language change requires constraints on what languages are
“legal” (addressing the _constraints_ problem in a way that bears upon the
behaviorism-nativism debate).

##### Current state

Language change has not received much attention in the literature; only two
papers were found in the survey which approached the topic specifically.
First, Graesser et al. (2019) study language contact, where two or more
populations of agents who have developed their own language in relative
isolation subsequently start communicating with each other. In particular, the
experiments replicated a handful of general language contact phenomena that
are known to occur with human language. First, while dialects start out as
mutually unintelligible, interaction between subsets of to populations can
cause convergence of all agents to a mutually intelligible language. Second,
when this contact occurs, either the larger population’s language will
dominate and take over the smaller population’s or a type of creole will form
with a lower overall complexity. Finally, when there is a linear chain of
populations, a continuum of mutual intelligibility emerges where populations
with fewer degrees of separation develop more similar dialects. These findings
primarily address the _embedding problem_ mentioned above.

Dekker & De Boer (2020) propose a set of of emergent communication experiments
studying a historical instance of language change, namely morphological
simplification in Alorese, a language of Eastern Indonesia. Specifically, the
experiments look to determine if adult language contact can explain the loss
of verb inflection in the whole language over time. The proposed approach is
based on deep neural networks and proposes leveraging cognitive two cognitive
mechanisms: Ullman’s declarative/procedural model of language learning
(Ullman, 2001b; a) and Lindblom’s H&H model (Lindblom, 1990).

##### Next steps

Emergent communication studies of the transition problem have the most
potential near-term progress. In particular, studies could investigate
quantitatively and at scale how transition between two stable states $s_{t-1}$
and $s_{t}$ takes place. Specifically, one could investigate whether two
languages coexist within a community of agents, with one gradually gaining
currency or first dominating a subgraph of the social network, or whether
changes happen abruptly across the whole population. Such studies with
emergent communication could then be compared to historical examples of the
transition program to verify and improve the effectiveness of emergent
communication approaches.

###  4.5 Language acquisition

##### Description

Language acquisition is the process by which a human acquires the ability to
use a new language. For this application, we will focus on first language
acquisition because it has weightier scientific implications than second
language acquisition and stands to gain more from emergent communication
techniques due to how it co-occurs with the acquisition of important non-
linguistic behaviors like reasoning and memory. Compared to the origin of
language (Section 4.3), observational data of first language acquisition data
is readily available as it always occurring in a population of humans.
Compared to the cognitive and perceptual aspects of language (Section 4.2),
there is more to be learned from direct observation of external behavior,
making the data easier to collect. Nevertheless, data on first language
acquisition is predominantly _observational_ , that is, not derived from
controlled, randomized experiments. Experiments which test anything more than
superficial aspects of language acquisition could have drastic negative
effects on human subjects and would be wholly unethical. Thus, data from more
involved experimental methods on first language acquisition has to come from
other sources such as neural networks trained on language data. Neural
networks trained purely on _text_ language data, though, fall far short of
human performance given a similar amount of language data, suggesting the non-
linguistic inputs might be key to replicating human language acquisition
(Warstadt & Bowman, 2022).

##### Applicability

Emergent communication naturally integrates non-linguistic inputs into
language (e.g., embodiment, interaction) into the acquisition of language by
the neural network agents instead of stipulating ahead of time how such inputs
will impact the emergent communication (Warstadt & Bowman, 2022; Bisk et al.,
2020). Furthermore, the ease of observing and experimenting with neural
networks vastly surpasses doing so with human subjects. These advantages of
using emergent communication as a simulation technique for studying language
acquisition are generally similar to those discussed in Origin of language
(Section 4.3) and Language, cognition, and perception (Section 4.2) (see those
sections for further details). While many of the advantages of emergent
communication techniques in studying language acquisition could be derived
from more traditional machine learning methods used on multi-modal data, these
traditional methods are only ever _mimicking_ the acquisition process that a
human goes through to acquire that human language (as a first language). On
the other hand, with emergent language acquisition, we can observe the
selfsame acquisition process that has formed the language in the first place
and not just an approximation thereof. This direct connection is important
since every step in empirical reasoning which involves approximations brings
with it more uncertainty in the conclusions.

##### Current state

Current literature has not often investigated language acquisition, so we will
address the collected work exhaustively. At the level of individuals, current
work has mainly looked at how the process of language acquisition interacts
with the emergence of compositionality and other properties of language.
Korbak et al. (2019; 2021) propose a developmentally-inspired curriculum which
breaks down language learning into multiple phases; they then show that this
method results in more compositional emergent communication. Cope & McBurney
(2022) present a method by which a new agent could acquire a pre-existing
emergent language purely through observation by inferring the intentions of
the observed agents. Kharitonov & Baroni (2020a) investigate a relationship in
the opposite direction, looking at how the degree of compositionality of a
language factors into the ease and speed of language acquisition. At a
population level, Li & Bowling (2019) investigate the same relationship
between ease of acquisition and compositionality in a generationally
transmitted setting, arguing (in line with Smith et al. (2003)) that the
pressure to acquire language from incomplete data can translate to a pressure
towards compositional language. Leaving aside compositionality, Portelance et
al. (2021) study the origin of shape bias, arguing that it can be explained
with communicative efficiency pressures rather than inductive biases in the
human or machine agents.

##### Next steps

The next steps for studying language acquisition are to demonstrate how
emergent communication techniques build directly on prior work studying deep
neural network-based models of language acquisition. Warstadt & Bowman (2022)
mention that neural networks hold potential for studying language learning but
also present a number of difficulties; thus future work in emergent
communication would do well to follow existing work on the topic closely (at
least for the near term). For example, Warstadt & Bowman (2020) determine that
a neural network (namely BERT) is able to make structural generalizations in
natural language but only after a observing more data than is developmentally
realistic. Similarly, Chang & Bergen (2022) compare word acquisition in
children and language models. In both cases, emergent communication could help
determine if the lack of embodiment and interactivity in standard language
model training explains part of why language models require significantly more
data than humans to acquire the same proficiency with language.

###  4.6 Linguistic variables

##### Description

Linguistic variables are the particular phenomena in language and its use
which are the subject of scientific study in linguistics. This is a catch-all
application which includes all studies seeking to determine the relationships
between linguistic and other linguistic/non-linguistic variables. These
variables span all of the various subfields of linguistics, forming a rough
low- to high-level hierarchy:

phonology

patterns of individual units of sound

morphology

patterns of individual units of meaning at the word and sub-word level

syntax

organization of words into meaningful structures (e.g., phrases, clauses,
sentences)

semantics

the inherent meaning of utterances in a language

pragmatics

meaning derived from context cues in conjunction with semantics

sociolinguistics

properties of language in the context of group and social dynamics

Beyond identifying individual relationships, broader questions within
linguistics concern patterns across relationships. In particular, a central
question across all of the above fields, has been the degree to which
linguistic variables are the product of formal properties of cognition
(formalism) and to what extent they are the emergent result of language use in
a communicative context (functionalism). For example, is the tendency of vowel
systems to be more-or-less maximally dispersed with the formant space a result
of formal universals such as a categorical phonological features that impose a
straitjacket on the realization of the vowels or a result—in language
evolution—of vowel distinctions that are not well-dispersed collapsing
(leaving only the well-dispersed vowels behind) (Blevins, 2004). 121212Or,
perhaps, due to a human drive to communicate as clearly as possible, given the
same investment of effort (Flemming, 2013).

Likewise, it has been observed that prefixes and suffixes (in words that have
more than one) are ordered so that those with the greatest relevance to the
meaning of the root are closest to the root. This has been attributed to a
formal constraint in which morphological scope mirrors syntactic scope (the
Mirror Principle) Baker (1985) or as a functional tendency based on a
motivation, on the part of speakers, to distribute information predictably so
that units of language are closest to the other units to which they are most
relevant (the Relevance Principle) (Bybee, 1985). This distribution is argued
to be the result of evolutionary processes emerging from attempts of language
users to communicate with one another (Bybee, 1985).

The evolution of pragmatics is even less-well understood. Is contextual
meaning a result of inherent principles of inference or is it an emergent
property of communicative interaction? Linguists have not been able to resolve
these issues experimentally because they involve simulating conversations
between speakers over decades and centuries—not interactions that can be
observed during an afternoon in the lab.

##### Applicability

In addition to the aforementioned applicable traits of emergent communication,
there are two ways in which emergent communication is particularly applicable
to studying linguistic variables. First, studying variables in any scientific
discipline requires isolating these variables from confounding factors. Within
emergent communication, it is possible to strip away confounding factors in
ways that are often not possible when studying humans directly.

Secondly, the holistic way in which emergent communication simulates
linguistic processes makes it particularly suitable to studying phenomena that
span multiple levels of the linguistic hierarchy. For example, the variables
relevant to the distinction between “who” and “whom” in modern English span
morphology (“-m” as an affix), syntax (“who” functioning as a subject or
object and “whom” as solely an object), and sociolinguistic (“whom” being
perceived as formal, dated, etc.). Emergent communication, by design, allows
for the interaction between many of the levels in the hierarchy without
stipulating a particular way in which they interact. On the other hand, more
traditional methods of modeling linguistic variables tend to be limited to
just the micro or macro scale, and any interaction between these has to be
determined ahead of time through handcrafted schemata, limiting the range of
potential outcomes.

##### Current state

Linguistic variables, broadly construed, show up frequently in the literature
as almost any property of emergent communication can be considered a
“linguistic variable”. For example, papers studying compositionality or
grounding are addressing a relationship between _syntax_ and _semantics_ while
papers looking at how to leverage extra-linguistic context for better
communication are addressing _pragmatics_. Nevertheless, we mention papers
here which directly tie into the study of human language and “linguistics” in
the narrower sense. Given that emergent communication is in the stage of
trying to look more like human language (cf. Section 2.1), the current
literature in this application primarily focuses on recreating established
linguistic phenomena in emergent communication settings. The following is list
of summarizing the existing literature:

phonology

In contrast to most emergent communication environments which have discrete
communication channels, Lan et al. (2020); Eloff et al. (2021) look at
continuous channels and discretization pressures analogous to the relationship
between phones and phonemes.

syntax

Chaabouni et al. (2019c) study whether or not emergent communication displays
word-order biases akin to many human languages. van der Wal et al. (2020)
analyze the output of unsupervised grammar induction applied to emergent
communication.

semantics

Chaabouni et al. (2021); Rita et al. (2020); Luna et al. (2020) study the
conditions under which Zipf’s Law of Abbreviation (Zipf, 1950) is present in
emergent communication. Kgebäck et al. (2018); Chaabouni et al. (2021) study
the way emergent communication divides up color spaces as compared to human
languages. Finally, Steinert-Threlkeld (2019) looks at the emergence of
function words in emergent communication as opposed to the exclusively
content-based words in most other settings.

sociolinguistics

Graesser et al. (2019); Kim & Oh (2021); Fulker et al. (2022) look at the
formation of dialects under different conditions in networks of interacting
agents. See “Current State” of Section 4.4 for Dekker & De Boer (2020).

##### Next steps

Phonology and morphology are relatively understudied in this area since most
emergent communication environments assume a one-to-one correspondence between
discrete symbols and “words”. The paradigm of discrete symbols-as-words
precludes analyzing sub-word components since a discrete symbol has no
structure. Thus, breaking away from this paradigm would open new avenues for
research into the phonological and morphological aspects of emergent
communication. This could be done either be simply analyzing discrete symbols
as sub-word units (requiring some other definition for what constitutes a word
in an emergent language), or by using a continuous communication channel with
some sort of discretization pressure (such that clusters of continuous signals
can be analyzed as discrete units).

Syntax and semantics are already studied in emergent communication, although
this research needs to be more tightly coupled with thoroughly linguistic
accounts of these phenomena instead of relying on looser, higher-level
analogies with linguistics. This is a non-trivial task insofar as the
definitions and models from linguistics will need to be adapted to the unique
difficulties of emergent communication. For example, emergent communication
can have radically different forms compared human language (or no organization
at all); this means that linguistic accounts may make assumptions about the
language being studied that do not necessarily hold for emergent communication
(e.g., languages are, at most, mildly context sensitive). Thus,
operationalizing linguistic definitions for emergent communication will
require expanding their scope to account for the numerous edge cases that
emergent communication presents.

For pragmatics and sociolinguistics, emergent communication environments will
generally have to incorporate more agents, temporality, and embodiment. This
is because these linguistic phenomena operate across many instances of
language use with a common context across time and among speakers (e.g.,
conversational, spatial, and cultural context). In contrast, many emergent
communication environments currently use single-step, simple observation, two-
agent environments which preclude observing almost all pragmatic and
sociolinguistic phenomena. The above point about linguistic definitions syntax
and semantics requiring adaptation to emergent communication holds true for
pragmatics and sociolinguistics as well since many behavior biases and
heuristics we observe in humans emergent communication agents may not possess
at all.

##  5 Discussion

###  5.1 Quantitative summary of results

In Figure 9, we present a quantitative summary of the categorization of papers
covered in our survey. Figure 9(a) shows at the number of paper falling within
the scope of each application, and Figure 9(b) further breaks the down
Linguistic variables (Section 4.6) into the different fields of linguistics.
Note that there is not a one-to-one correspondence between papers and
applications, a paper may have no applications if its contributions are not
properly applications or more than one application if its contribution touches
on multiple areas.

9cm][c]0.4

$0$$10$$20$$30$LinguisticsAcquisitionChangeOriginCognitionExpl.
MLHCIMACSyntheticToolingTheoryMetricsRederiv.$3$$8$$21$$0$$4$$6$$16$$7$$16$$6$$2$$11$$29$Number
of Papers

(a) Number of papers corresponding to each section of this review.

9cm][c]0.5

$0$$10$$20$$30$$40$Phon.Morph.SyntaxSemanticsComp.Prag.Socio.$1$$1$$3$$35$$46$$22$$7$$2$$3$$6$$1$$4$Number
of PapersLing.-FocusedLing.-Related

(b) Breakdown of linguistic variables papers; “Linguistic-Focused” refers to
papers discussed in Section 4.6, while “Linguistic-Related” includes anything
more loosely related to that area of linguistics.

Figure 9: Quantitative summary of paper topics in this survey. Abbreviations:
_MAC_ : multi-agent communication, _HCI_ : human–computer interaction, _Expl.
ML_ : explainable machine learning, and _Comp._ : compositionality.

###  5.2 Internal goals

The internal goals of emergent communication prove tricky for this survey
since they both make up the majority of contributions in emergent
communication papers but only loosely qualify as applications. Many of the
papers we surveyed listed contributions along the lines of “introducing an
environment where we can observe $X$ phenomenon” or “demonstrating a
relationship between variable $X$ in the environment and variable $Y$ in the
emergent language”. The second of these was by the most common in the $245$
emergent communication papers surveyed: it appeared $116$ times whereas the
next highest category was “related to compositionality” with only $46$ papers.
This is not to say that these contributions are unimportant or unnecessary,
but they fail to be true applications in the sense of being a focused “goal”
which a line of research can pursue. Thus, such contributions were omitted
from this survey.

Aside from these non-application contributions, the topic of _metrics_ was the
most common. Many of these metrics, though, are not treated as applications or
goals in themselves as they are introduced for the needs of the paper and do
not see reuse in subsequent papers. Nevertheless, some papers do explicitly
aim towards better metrics, comparing the quality of metrics in an effort to
refine the tools researchers have for analyzing emergent communication (Lowe
et al., 2019; Korbak et al., 2020).

Finally, Rederiving human language (Section 2.1) was one goal which we
included in this paper, functioning more like a position paper than a survey
paper. This goal did was not explicitly pursued by any of the papers we
reviewed, although it is implicit in a large number of papers, namely those
which seek to align emergent communication with some human language-like
quality (e.g., compositionality, Zipf’s Law of Abbreviation). Nevertheless, we
argue that rederiving human language should receive more attention which
addresses it holistically. This is because (1) it is critical to making
possible the downstream task- and knowledge-driven applications and (2) it is
an effective way to interpret the other contributions falling under the
“internal” umbrella.

###  5.3 Task-driven applications

Within the task-driven applications we find that multi-agent communication has
received the most attention. This is generally expected as it is one the most
natural applications of emergent communication, given its foundation in deep
multi-agent reinforcement learning. Although some papers have addressed using
emergent communication for synthetic data, it is somewhat surprising that the
number is not higher since it is probably the application with the most
potential for near-term success, especially in low-resource domains as a
replacement for traditional synthetic data. Interacting with humans, while an
important long-term goal, does not hold as much short-term promise because it
is more difficult to conduct scientific studies with humans and anything short
of near-human language-like emergent communication is not going to surpass
other methods for interfacing with humans through natural language.

###  5.4 Knowledge-driven applications

Within the knowledge-driven the applications, we find the cognitive and core
linguistic aspects of emergent communication to the most addressed. The number
shown for “Cognition” in Figure 9(a) includes paper using a broader sense of
“cognition” and “cognitive science” including topics like perception, internal
representations, and neural architectures. 131313Although we did not perform
the same focused-versus-related breakdown with cognition as was done with
linguistic variables, we expect we would have found a similar divide with many
cognition-related papers and only a handful of papers which focus on issues
directly relevant to cognitive science (as illustrated in Figure 9(b)). As
shown in Figure 9(b), core linguistics, when given this broader interpretation
is far more prevalent with over $100$ in total. By comparison, language change
and language acquisition of language are more niche and have fewer papers
associated with them.

Within the umbrella of linguistic variables, we can see a handful of trends.
First, we see generally in Figure 9(b) that the number of papers which address
variables directly relevant to linguistics is dwarfed by the number of papers
which take only a loose inspiration from linguistics. Compositionality is
especially interesting in this regard as it is, by far, the most written-about
topic under the broad umbrella of linguistics, yet we did not find any papers
addressing it from a strongly linguistic and human language-oriented
perspective.

This may be due, in part, to the fact that human languages are universally
compositional and generally have similar methods of composing meaning at a
broad level in comparison to many ways in which emergent communication may or
may not be compositional. Aside from compositionality, semantics and
pragmatics are the most studied topics. These areas of linguistics naturally
line up with the most foundational aspects of emergent communication, namely
figuring out what emergent languages are actually communicating (semantics)
and how this meaning derives from communication strategies and environmental
pressures (pragmatics). Finally, phonology and morphology have the least
amount of work focused on them. One potential reason for this is that emergent
communication systems are typically structured in a way to preclude phonology
by using discrete communication channels and morphology by assuming discrete
symbols to already be individual units of meaning (i.e., morphemes) without
investigating potential subword structure further.

##  6 Conclusion

In this paper, we have given a comprehensive summary of the goals and
applications of deep learning-based emergent communication research. The
applications of emergent communication can roughly be categorized into those
which aim at: improving emergent communication techniques themselves
(internal); solving well-defined, practical problems (task-driven); and
expanding human knowledge of the natural world (knowledge-driven). Each of
these applications has been accompanied by a description of its scope, an
explication of emergent communication’s unique role in addressing it, a
summary of the extant literature working towards the application, and brief
recommendations for near-term research directions. Finally, we identify
general trends observed in the course of surveying the applications of
emergent communication.

This work has three primary goals. First, it is meant to inspire future
emergent communication research by compiling the most salient areas of
research into a single document with relevant work cited. Second, this work is
meant to accessibly illustrate the potential applications of emergent
communication to practitioners who are not as familiar with the multi-agent
reinforcement learning or deep learning in general. Finally, defining the
ultimate aims of emergent communication is critical to guiding the field of
research itself through practices like evaluation metrics and benchmarks.
Evaluation metrics require explicitly defining what a _good_ or _desirable_
emergent language is, and understanding what emergent communication can be
used for is a foundational step. While this paper does not come close to
exhausting the nuances of each of these applications, it highlights the nature
and importance of applications as a whole in order to serve the future of
emergent communication research.

#### Acknowledgements

We would like to thank that anonymous reviewers for their painstaking,
thorough, and very constructive comments. Responding to their insightful
feedback has made this article much better. This material is based on research
sponsored in part by the Air Force Research Laboratory under agreement number
FA8750-19-2-0200. The U.S. Government is authorized to reproduce and
distribute reprints for Governmental purposes notwithstanding any copyright
notation thereon. The views and conclusions contained herein are those of the
authors and should not be interpreted as necessarily representing the official
policies or endorsements, either expressed or implied, of the Air Force
Research Laboratory or the U.S. Government.

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