# A Survey of Knowledge-Intensive NLP with Pre-Trained Language Models

Da Yin1111The work was mainly done during internship at Microsoft. Li Dong2
Hao Cheng2 Xiaodong Liu2 Kai-Wei Chang1 Furu Wei2 Jianfeng Gao2
1University of California, Los Angeles
2Microsoft Research
{da.yin<EMAIL_ADDRESS>{lidong1, chehao, xiaodl, fuwei,
<EMAIL_ADDRESS>
###### Abstract

With the increasing of model capacity brought by pre-trained language models,
there emerges boosting needs for more knowledgeable natural language
processing (NLP) models with advanced functionalities including providing and
making flexible use of encyclopedic and commonsense knowledge. The mere pre-
trained language models, however, lack the capacity of handling such
knowledge-intensive NLP tasks alone. To address this challenge, large numbers
of pre-trained language models augmented with external knowledge sources are
proposed and in rapid development. In this paper, we aim to summarize the
current progress of pre-trained language model-based knowledge-enhanced models
(PLMKEs) by dissecting their three vital elements: knowledge sources,
knowledge-intensive NLP tasks, and knowledge fusion methods. Finally, we
present the challenges of PLMKEs based on the discussion regarding the three
elements and attempt to provide NLP practitioners with potential directions
for further research.

##  1 Introduction

Pre-trained language models (Devlin et al., 2019; Radford et al., 2019) have
achieved enormous advances for natural language processing (NLP). Using
various types of self-supervised language modeling objectives over massive
text corpora, the resulting models provide powerful text representations for
supervised fine-tuning on downstream tasks, leading to great successes on a
wide range of NLP tasks (Wang et al., 2018, 2019). In particular, recent study
shows that certain knowledge (linguistic or factual knowledge (Manning et al.,
2020; Petroni et al., 2019; Roberts et al., 2020; Dai et al., 2021)) is
implicitly stored in their parameters which partially explains the better
generalization abilities of NLP models based on pre-trained language models.
However, the machines merely based on the implicit knowledge from pre-trained
language models pose challenges for dynamically updating learned knowledge
(e.g., correcting biases or enriching knowledge sources), which may not
satisfy users’ need in real-world applications, such as serving our daily
information seeking routines (Guu et al., 2020). In other words, one main
drawback of existing pre-trained language models is the lack of ability to
leverage explicit encyclopedic and commonsense knowledge.

The growing real-world needs form the motivation to investigate Pre-Trained
Language Model-based Knowledge-Enhanced Models (PLMKEs). Here, we establish
the framework of PLMKE for NLP, involving a knowledge interface responsible
for fetching relevant _external knowledge_ with text inputs and a _knowledge
fusion module_ integrating knowledge with the representations learned from
pre-trained language models. Recently, PLMKEs have been widely used in various
knowledge-intensive tasks including open-domain question answering (Guu et
al., 2020; Lewis et al., 2020; Cheng et al., 2021), fact verification (Zhou et
al., 2019b; Liu et al., 2020), entity linking (Jiang et al., 2021) and
commonsense reasoning (Lin et al., 2019; Yasunaga et al., 2021).

Given that different tasks require various kinds of knowledge, most recent
PLMKEs customize the source for knowledge interface and the design of
corresponding knowledge fusion method. Thus, we structure our survey so that
different knowledge-intensive scenarios can be understood through our lens.
Specifically, there are three vital items related to PLMKEs:

#### Knowledge Sources:

Knowledge sources (Wikipedia, (Bollacker et al., 2008; Vrandečić, 2012; Speer
et al., 2017; Sap et al., 2019a; Zhang et al., 2020a), etc.) provide external
knowledge to PLMKEs and lay the foundations of PLMKEs along with pre-trained
language models. The contents of knowledge sources are also decisive to the
tasks PLMKEs can solve.

#### Knowledge-Intensive NLP Tasks:

Knowledge-intensive NLP tasks ((NIST, 2004; Thorne et al., 2018; Kwiatkowski
et al., 2019; Petroni et al., 2021), etc.) are testbeds to evaluate the
performance of PLMKEs on checking whether the selected knowledge sources are
appropriate and manifest the effectiveness of knowledge fusion methods.

#### Knowledge Fusion Methods:

Knowledge fusion methods ((Zhang et al., 2019; Lin et al., 2019; Guu et al.,
2020; Sun et al., 2021), etc.) involve the implementations of exploiting
knowledge sources to empower pre-trained language models in PLMKEs and achieve
better performance on knowledge-intensive NLP tasks.

In this paper, we aim to provide researchers a general view on PLMKEs. The
survey centers on the aforementioned three elements, knowledge sources,
knowledge-intensive NLP tasks, and knowledge fusion methods. We will discuss
the following questions in the rest of sections.

  * •

Section 2 (Knowledge Sources): What are the types of commonly used knowledge
sources? What is the format of knowledge stored in the knowledge sources? What
are the characteristics of the knowledge sources?

  * •

Section 3 (Knowledge-Intensive NLP Tasks): What are the common knowledge-
intensive NLP tasks PLMKEs are applied on? What knowledge is useful to solve
the tasks?

  * •

Section 4 (Knowledge Fusion Methods): How can we categorize the numerous
knowledge fusion methods? What do the different categories of fusion methods
typically do when fusing knowledge? What are fusion methods adopted in common
PLMKEs and why?

  * •

Section 5 (Challenges and Future Directions): What are the potential
challenges of PLMKEs? What are the future directions to solve these
challenges?

We notice that there are two contemporaneous surveys (Wei et al., 2021; Yang
et al., 2021) about knowledge-enhanced NLP models. However, they both focus on
the models that incorporate knowledge during pre-training stage, which are a
subset of the scope that our survey studies. Besides, we adopt a novel
taxonomy to dissect and categorize PLMKEs and propose new perspectives for the
future directions upon the taxonomy.

##  2 Knowledge Sources

Knowledge sources provide pre-trained language models with needed knowledge
and empower them with higher capability to handle knowledge-intensive NLP
tasks. We list the common knowledge sources leveraged in PLMKEs in Table 1,
and further categorize them into two groups: encyclopedic knowledge and
commonsense knowledge.

###  2.1 Encyclopedic Knowledge

Encyclopedic knowledge contains attributes (e.g., age, duration) about
entities (e.g., person, event) and the relations (e.g., educated at, followed
by) between entities. Wikipedia is one of the prevalent knowledge sources
providing massive amount of encyclopedic knowledge including biography of a
person and background of an event. Extracted from unstructured text corpora,
factual knowledge bases aim at uncovering graph structures of entities and
converting them into structured database. Typically, structured encyclopedic
knowledge is represented by triplets containing entity names and their
relationships (e.g., <Tom Hanks, occupation, actor>). Factual knowledge bases
(e.g., Wikidata) are also widely used in PLMKEs (Peters et al., 2019; Agarwal
et al., 2021).

Knowledge Types | Knowledge Sources | Knowledge Domains
---|---|---
|  Wikipedia/Wikidata Vrandečić (2012) |
|  DBPedia Auer et al. (2007) | open domain
Encyclopedic Knowledge |  Freebase Bollacker et al. (2008)
UMLS Bodenreider (2004) | biomedicine
| AMiner
---
Tang et al. (2008)
science
ConceptNet Speer et al. (2017) |
TransOMCS Zhang et al. (2020a) | open domain
Commonsense Knowledge |  CSKG Ilievski et al. (2021)
ATOMIC Sap et al. (2019a) | human interaction
ATOMIC${}^{20}_{20}$ Hwang et al. (2021)
ASER Zhang et al. (2020b) | eventuality
Table 1: Common knowledge sources used in PLMKEs.

###  2.2 Commonsense Knowledge

Commonsense knowledge includes the basic facts about situations in human’s
daily life. It involves everyday events and their effects (e.g., mop up the
floor if we split food over it), facts about beliefs and desires (e.g., study
hard to win scholarship), and properties of objects (e.g., goat has four
legs). Thus, different from encyclopedic knowledge, commonsense knowledge is
usually shared by most people and implicitly assumed in communications.

Similar to the storage method of factual knowledge bases, commonsense
knowledge sources also use triplets to represent knowledge. These sources
depict commonsense including subtype relationship between objects (e.g.,
<apple, IsA, fruit>in ConceptNet), cause and effect of an event (e.g.,
<personX adopts a pet, Effects, play with the pet>), and intent of human
behaviour (e.g., <personX adopts a pet, Causes, to have a companion>). We can
observe that the main difference from factual knowledge bases is that the
triplets contain everyday objects and their elements are typically described
with a short sentence. Recent PLMKEs (Lin et al., 2019; Mitra et al., 2019)
mostly utilize the sources including ConceptNet and ATOMIC as external
knowledge to enhance models’ commonsense reasoning capacity.

###  2.3 Characteristics of Current Knowledge Sources

We discuss two typical characteristics of current knowledge sources: large-
scale and diverse. Regarding to the scale of knowledge sources, all the
encyclopedic knowledge sources in Table 1 contain millions of concepts and at
least hundred million of facts induced by them. The largest commonsense
sources in Table 1 is ASER, which contains 64 million facts. We observe that
the size of commonsense knowledge sources is much smaller than the
encyclopedic ones. However, compared with prior commonsense sources such as
Cyc (Lenat, 1995), the current sources are produced in more precise and
scalable way: the annotation process is partially automatic and accessible to
non-experts. The current trend of commonsense collection methods manifests the
potential to scale up the knowledge sources.

The domains that common knowledge sources cover are diverse. Encyclopedic
knowledge sources such as Wikipedia, DBPedia and Freebase are collected from
open domain, suggesting that they are constructed by heterogeneous knowledge
not limited to specific domains. Meanwhile, knowledge sources involving
specific domains such as biomedicine and science (e.g., UMLS and AMiner) are
established to boost the development of domain-specific applications. Since
commonsense involves various aspects including human interaction and object
properties in everyday life, there exist both open-domain commonsense
knowledge sources (e.g., ConceptNet, TransOMCS) that cover multiple domains of
commonsense, and domain-specific commonsense sources (e.g., ATOMIC, ASER) that
focus on particular types. The diversity of knowledge sources is beneficial
for laying the foundations of broader future applications of PLMKEs.

##  3 Knowledge-Intensive NLP Tasks

Knowledge-intensive NLP tasks are served as testbeds to evaluate the
capability of PLMKEs to solve problems that require external knowledge. In
this section, we provide an overview of knowledge-intensive NLP tasks, and
summarize their corresponding features.

###  3.1 Overview of Knowledge-Intensive NLP Tasks

Knowledge-intensive NLP tasks can be divided into two groups based on the
types of the required knowledge: encyclopedic and commonsense knowledge-
intensive tasks.

Table 2 lists several typical datasets of three representative encyclopedic
knowledge-intensive tasks: open-domain question answering (QA), fact
verification, and entity linking. The open-domain QA task aims to answer
information seeking questions (e.g., “When was Barack Obama born?”) that
require encyclopedic knowledge over various domains. Fact verification (e.g.,
judge if the claim “Barack Obama born was born on August 4 1961.” is true) is
designed to verify whether a given text claim is factually correct which also
demands the model’s ability to reason over large amount of factual knowledge.
Lastly, the purpose of entity linking system is to link text mentions of
entities to their corresponding unique identifier in a target database, e.g.,
linking to Wikipedia pages. The datasets (Thorne et al., 2018; Kwiatkowski et
al., 2019; Guo and Barbosa, 2014) for these tasks heavily relies on
encyclopedic knowledge sources including Wikipedia and DBPedia.

Commonsense knowledge-intensive tasks focus on testing whether models can
accurately understand and respond to daily scenarios. For example, in a task
about social commonsense, given a context like “Someone spilled the food all
over the floor”, models are required to select the most proper responses like
“He/she needs to mop up” instead of unreasonable ones like “Run around in the
mess”. The types of commonsense-intensive tasks are diverse because of the
diversity of commonsense knowledge. As shown in Table 3, tasks about social
commonsense involve human interactions and thoughts; when it comes to physical
commonsense, the questions in the datasets inquire physical properties and
ways to manipulate objects; temporal commonsense reasoning datasets usually
contain questions about event order, duration and frequency.

Tasks | Datasets | Data Sources
---|---|---
|  | Natural Questions
---
Kwiatkowski et al. (2019)
Wikipedia
Open-domain QA |  | HotpotQA
---
Yang et al. (2018)
Wikipedia
| FEVER
---
Thorne et al. (2018)
Wikipedia
Fact Verification |  | BoolQ
---
Clark et al. (2019)
Wikipedia
| ACE2004
---
NIST (2004)
news
Entity Linking |  | AIDA CoNLL-YAGO
---
Hoffart et al. (2011)
DBPedia & YAGO Suchanek et al. (2007)
| WnWi
---
Guo and Barbosa (2014)
Wikipedia
| WnCw
---
Guo and Barbosa (2014)
Clueweb 111https://lemurproject.org/clueweb12/

Table 2: Detailed information about representative encyclopedic knowledge-
intensive tasks and datasets.

Commonsense Types | Tasks/Datasets | Data Sources
---|---|---
|  | CommonsenseQA
---
Talmor et al. (2019)
ConceptNet
General Commonsense |  | WSC
---
Levesque et al. (2012)
human thoughts
|  $\alpha$NLI
---
Bhagavatula et al. (2020)
ROCStories Mostafazadeh et al. (2016)
| CommonGen
---
Lin et al. (2020)
image captions & ConceptNet
Social Commonsense |  | SocialIQA
---
Sap et al. (2019b)
ATOMIC Sap et al. (2019a)
Physical Commonsense |  | PIQA
---
Bisk et al. (2020)
image captions & ConceptNet
| HellaSWAG
---
Zellers et al. (2019)
video captions
Temporal Commonsense |  | McTaco
---
Zhou et al. (2019a)
MultiRC Khashabi et al. (2018)
| TACRIE
---
Zhou et al. (2021)
ROCStories Mostafazadeh et al. (2016)

Table 3: Detailed information about types of commonsense and representative
commonsense knowledge-intensive tasks.

###  3.2 Characteristics of Knowledge-Intensive Tasks

The most salient characteristic is that external encyclopedic or commonsense
knowledge is necessary to perform well on these tasks. Not only for models, it
is hard for humans to answer questions about Barack Obama’s birth date without
any reference knowledge. Another characteristic is that although these tasks
can be tackled with external knowledge, the required knowledge may not be
directly given along with input. In other words, the default task input is
simply a question or context of a certain scenario, without any additional
information. It motivates researchers to consider adding a module in charge of
grounding to external knowledge sources in the design of PLMKEs.

##  4 Knowledge Fusion Methods

Tackling knowledge-intensive NLP tasks highly relies on the assistance of
appropriate knowledge sources. How to fuse such important knowledge into the
strong pre-trained language models and make them more knowledgeable poses
challenges to researchers.

The training process of pre-trained language models usually involve two
stages: pre-training and fine-tuning (Devlin et al., 2019). Pre-training is a
self-supervised learning process to learn representations through language
modeling on large unlabeled text corpus. Fine-tuning is the process to adapt
pre-trained models with task-specific supervision on downstream tasks. In
PLMKEs, knowledge can be integrated in either stage but the fusion methods for
the two stages are quite different. In this section, we categorize the
mainstream knowledge fusion methods by the stage where knowledge is fused in
PLMKEs.

Figure 1: Pre-fusion methods.

###  4.1 Pre-Fusion Methods

Pre-fusion methods fuse external knowledge in the pre-training stage (Zhang et
al., 2019; Sun et al., 2021). Before knowledge fusion, knowledge sources are
first processed into the format similar to unstructured raw text corpus. Then,
the processed text corpus are used for further pre-training with a sharing set
of learning objectives used by original language models. Thus, pre-fusion
methods enable knowledge fusion without much architectural change. For
knowledge sources such as knowledge graphs, however, the knowledge is usually
structured and not aligned with the unstructured input format of language
models. The simplest approach to tackling this challenge is concatenating the
entities and relation (Zhang et al., 2019) or generating fluent synthetic
sentences by conditional text generation models (Agarwal et al., 2021).

Tasks | Datasets | Models | Fusion Types | Fused Knowledge
---|---|---|---|---
Open-domain QA |  Natural Questions Kwiatkowski et al. (2019) |  UnitedQA Cheng et al. (2021) | Post-fusion | Wikipedia
WebQuestion Berant et al. (2013) |  EMDR2 Sachan et al. (2021) | Post-fusion | Wikipedia
TriviaQA Joshi et al. (2017) |  EMDR2 Sachan et al. (2021) | Post-fusion | Wikipedia
Other Representative Models |  REALM Guu et al. (2020) | Hybrid-fusion | Wikipedia
RAG Lewis et al. (2020) | Hybrid-fusion | Wikipedia
Fact Verification |  FEVER Thorne et al. (2018) |  Jiang et al. (2021) | Post-fusion | Wikipedia
BoolQ Berant et al. (2013) |  ERNIE 3.0 Sun et al. (2021) | Pre-fusion |  Wikipedia, Bookcorpus Zhu et al. (2015), etc.
Other Representative Models |  GEAR Zhou et al. (2019b) | Post-fusion | Wikipedia
KGAT Liu et al. (2020) | Post-fusion | Wikipedia
Entity Linking |  AIDA CoNLL-YAGO Hoffart et al. (2011) | Mulang’ et al. (2020) | Post-fusion | Wikipedia
Other Representative Models |  CHOLAN Ravi et al. (2021) | Post-fusion | Wikipedia

Table 4: State-of-the-art PLMKEs on encyclopedic knowledge-intensive tasks and
other representative models. Figure 2: Post-fusion methods. Path A indicates
structure-aware post-fusion methods that encode structured knowledge into
supplemental embeddings and integrate with the text embeddings produced by
pre-trained language models. Path B indicates text-based post-fusion methods
that retrieve relevant texts and subgraphs and convert them into texts to be
fed into pre-trained language models. Commonly, each PLMKE chooses either path
A or B to fuse knowledge.

###  4.2 Post-Fusion Methods

Instead of incorporating knowledge in the pre-training stage, post-fusion
methods seek to fuse the knowledge in the fine-tuning stage (Zhou et al.,
2019b; Lin et al., 2019; Liu et al., 2020; Cheng et al., 2021). Given an input
text from a particular knowledge-intensive task, post-fusion methods first
retrieve the relevant knowledge to the input, and then perform a joint
reasoning on top of the augmented input.

To capture relevant knowledge, the post-fusion methods leverage text retriever
on unstructured knowledge sources to extract the useful textual spans; for
structured knowledge sources, previous works attempt to match the entities
appearing in input text to the relevant entity-centric subgraphs. After
capturing the knowledge, they select one of the two following approaches to
implementing knowledge fusion. The captured knowledge can be transformed into
knowledge embeddings by encoders such as graph neural networks, and used as
supplemental features to the text embeddings provided by pre-training language
models for the following reasoning modules (structure-aware post-fusion: path
A in Figure 2) (Lin et al., 2019; Yasunaga et al., 2021). It can also be
directly concatenated with the text input and fed into the pre-training
language models altogether (text-based post-fusion: path B in Figure 2)
(Karpukhin et al., 2020; Cheng et al., 2021).

###  4.3 Hybrid-Fusion Methods

Hybrid-fusion methods are a combination of pre-fusion and post-fusion methods:
knowledge is fused in both pre-training and fine-tuning stages. Although there
exists salient difference between pre-fusion and post-fusion methods, the
hybrid-fusion methods enable us to unify both: the retriever frequently
leveraged in post-fusion methods can be jointly trained in the pre-training
stage. That is, during the pre-training, language models are also learning to
leverage additional retrieved knowledge for modeling the language context. The
pre-trained model augmented by the jointly learned retriever can thus utilize
the knowledge from the retriever more effectively during the fine-tuning
stage. The retrieval-augmented pre-training (Peters et al., 2019; Guu et al.,
2020; Lewis et al., 2020) is commonly adopted in hybrid-fusion methods and it
manifests the effectiveness on several knowledge-intensive tasks discussed in
Section 3.

Figure 3: Hybrid-fusion methods.

Commonsense Types | Tasks/Datasets | Models | Fusion Types | Fused Knowledge
---|---|---|---|---
General Commonsense |  CommonsenseQA Talmor et al. (2019) |  GreaseLM Zhang et al. (2022) | Post-fusion | ConceptNet
WSC Levesque et al. (2012) |  ERNIE 3.0 Sun et al. (2021) | Pre-fusion |  Wikipedia, Bookcorpus Zhu et al. (2015), etc.
$\alpha$NLI Bhagavatula et al. (2020) |  UNIMO Li et al. (2021) | Pre-fusion | Wikipedia, Bookcorpus, images
Other Representative Models |  KagNet Lin et al. (2019) | Post-fusion | ConceptNet
QA-GNN Yasunaga et al. (2021) | Post-fusion | ConceptNet
Social Commonsense |  SocialIQA Sap et al. (2019b) |  UNICORN Lourie et al. (2021) | Pre-fusion | various commonsense benchmarks
Other Representative Models |  UnifiedQA-11B Khashabi et al. (2020) | Pre-fusion | various QA benchmarks
McQueen Mitra et al. (2019) | Post-fusion | ATOMIC
Physical Commonsense |  PIQA Bisk et al. (2020) |  UNICORN Lourie et al. (2021) | Pre-fusion | various commonsense benchmarks
Other Representative Models |  UnifiedQA-11B Khashabi et al. (2020) | Pre-fusion | various QA benchmarks

Table 5: State-of-the-art PLMKEs on commonsense knowledge-intensive tasks and
other representative models.

###  4.4 Representative Models for Specific Tasks

Here, we first categorize representative models for various knowledge-
intensive NLP tasks based on their corresponding knowledge fusion methods
discussed in previous section. Table 4 and 5 list state-of-the-art (SOTA)
PLMKEs on encyclopedic and commonsense knowledge-intensive tasks and several
representative models, respectively. For encyclopedic knowledge-intensive
tasks, it is shown that except BoolQ, the other state-of-the-art models all
adopt post-fusion methods. On the contrary, for commonsense knowledge-
intensive tasks, except CommonsenseQA, pre-fusion methods are broadly
leveraged in the SOTA models.

We then analyze why pre-fusion methods are not prevalent and effective on
encyclopedic knowledge-intensive tasks. In pre-fusion methods, the knowledge
required in these tasks is implicitly stored in pre-trained parameters. But it
is hard to determine what the knowledge is finally stored, and it also
increases the difficulty in eliciting and leveraging the knowledge (Guu et
al., 2020; Wang et al., 2021). Instead, post-fusion methods are able to infer
upon explicit and concrete textual knowledge. But the advantage of post-fusion
to leverage explicit and concrete knowledge may become a shortcoming for
commonsense knowledge-intensive tasks. As mentioned in Section 2, commonsense
is usually implicit inside texts and the coverage of commonsense knowledge
sources is much smaller than that of encyclopedic knowledge sources. Even if
we compose large-scale commonsense knowledge sources with the help of
knowledge acquisition methods, we are still likely to miss a large body of
commonsense knowledge used in our daily life. Therefore, it is possible that
post-fusion methods may fail at retrieving the relevant knowledge inside the
knowledge sources and thus cannot bring extra benefits to the pre-trained
language models.

##  5 Challenges and Future Directions

###  5.1 Unified PLMKEs Across Tasks and Domains

Recent developments of PLMKEs have led to task-specific modeling advances. As
shown in Table 4, the models frequently used on encyclopedic knowledge-
intensive tasks adopt post-fusion and hybrid-fusion methods, while the two
fusion methods are not usually exploited on commonsense knowledge-intensive
tasks. Furthermore, we observe that the state-of-the-art models in different
knowledge-intensive NLP tasks are unique, making the progress on each task
seemingly incompatible. Beyond the tasks listed in Table 2 and 3, knowledge-
intensive NLP tasks are extended to various domains involving biomedical and
legal (Liu et al., 2021b) knowledge. Recently, researchers also attach more
importance to the diversity of knowledge existing in different times (Dhingra
et al., 2021) and regions (Zhang and Choi, 2021; Yin et al., 2021; Liu et al.,
2021a). The diversity across tasks and domains is naturally fostering the need
for unified PLMKEs, instead of promoting the trend of devising unique models
on individual tasks.

###  5.2 Reliabilty of Knowledge Sources

Since knowledge sources are the basis of PLMKEs, we are concerned with the
reliability of knowledge sources. Currently, many large-scale knowledge
sources are constructed by automatic knowledge acquisition algorithms.
Whereas, there exists a trade-off between the scale and precision: it is
likely to introduce false and biased information into the knowledge sources
(Sun and Peng, 2021). We anticipate bias amplification in case the PLMKEs are
constructed on biased knowledge sources. We call upon researchers to be aware
of the reliability of knowledge sources via proposing more precise knowledge
acquisition algorithms and careful inspection over the knowledge sources they
intend to use.

###  5.3 Reasoning Module Design

Reasoning is an important step for solving knowledge-intensive NLP tasks. It
is essential especially for commonsense knowledge-intensive tasks, since the
relevant commonsense knowledge is usually implicit and should be used in
multiple turns of reasoning. When we humans encounter the situation like
“Someone spilled the food all over the floor”, we are first aware of the fact
that the floor is not clean, and others’ shoes would get dirty if they stepped
on the spilled food. Based on the situation, the intent to mop up the floor
produces. Though several PLMKEs achieve great performance on such commonsense-
related scenarios, it is unclear about whether the models perform human-like
reasoning implicitly or simply capture the spurious correlation and thus not
robust upon more complex situations. Existing models containing reasoning
modules mainly perform reasoning on entity (Lin et al., 2019; Yasunaga et al.,
2021) or syntax structures (Yin et al., 2020; Bai et al., 2021), which cannot
cover the complex situations like the aforementioned “spill food” example. To
achieve the human-like capability of recognizing the everyday situations,
multi-hop reasoning module is needed for designing trustworthy PLMKEs that can
simulate human thoughts.

##  6 Conclusions

We comprehensively survey existing works about knowledge-intensive NLP with
pre-trained language models and summarize the current progress in terms of the
three critical components in PLMKEs: knowledge sources, knowledge-intensive
NLP tasks, and knowledge fusion methods. Based on the discussion about the
three components, we further pose several challenges that would be influential
in the practical usage and propose the related future directions in response
to the challenges. We hope that this paper could provide NLP practitioners
with a clear picture on the topic and boost the development of the current
knowledge-intensive NLP technologies.

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