# Towards Explainable Evaluation Metrics for Machine Translation

Christoph Leiter1, Piyawat Lertvittayakumjorn2, Marina Fomicheva3,
Wei Zhao4, Yang Gao5, Steffen Eger1
1Bielefeld University, 2Imperial College London, 3University of Sheffield
4Heidelberg Institute for Theoretical Studies, 5Royal Holloway, University of
London

###### Abstract

Unlike classical lexical overlap metrics such as BLEU, most current evaluation
metrics for machine translation (for example, COMET or BERTScore) are based on
black-box large language models. They often achieve strong correlations with
human judgments, but recent research indicates that the lower-quality
classical metrics remain dominant, one of the potential reasons being that
their decision processes are more transparent. To foster more widespread
acceptance of novel high-quality metrics, explainability thus becomes crucial.
In this concept paper, we identify key properties as well as key goals of
explainable machine translation metrics and provide a comprehensive synthesis
of recent techniques, relating them to our established goals and properties.
In this context, we also discuss the latest state-of-the-art approaches to
explainable metrics based on generative models such as ChatGPT and GPT4.
Finally, we contribute a vision of next-generation approaches, including
natural language explanations. We hope that our work can help catalyze and
guide future research on explainable evaluation metrics and, mediately, also
contribute to better and more transparent machine translation systems.

##  1 Introduction

The field of evaluation metrics for Natural Language Generation (NLG),
especially machine translation (MT) is in a crisis (Marie et al. 2021).111We
published an earlier version of this paper (Leiter et al. 2022a) under a
different title. Both versions consider the conceptualization of explainable
metrics and are overall similar. However, while the old version contains
several novel experiments, this new version puts a stronger emphasis on the
survey of approaches for the explanation of MT metrics including the latest
LLM based approaches. For example, this comprises techniques that return fine-
grained error labels or natural language explanations. Despite the development
of multiple high-quality evaluation metrics in recent years (Zhao et al. 2019;
Zhang et al. 2020a; Rei et al. 2020; Sellam et al. 2020; Yuan et al. 2021),
the Natural Language Processing (NLP) community appears hesitant to adopt them
for assessing NLG systems (Marie et al. 2021; Gehrmann et al. 2023). Empirical
investigations of Marie et al. (2021) indicate that the majority of MT papers
relies on surface-level evaluation metrics such as BLEU and METEOR (Papineni
et al. 2002; Banerjee and Lavie 2005), which were created two decades ago, a
trend that may allegedly have worsened in recent times. These surface-level
metrics cannot (even) measure semantic similarity of their inputs and are thus
fundamentally flawed, particularly when it comes to assessing the quality of
recent state-of-the-art MT systems (e.g. Peyrard 2019; Freitag et al. 2022),
raising concerns about the credibility of the scientific field. We argue that
the potential reasons for this neglect of recent high-quality metrics include:
(i) non-enforcement by reviewers; (ii) easier comparison to previous research,
for example, by copying BLEU-based results from tables of related work
(potentially a pitfall in itself); (iii) computational inefficiency to run
expensive new metrics at large scale; (iv) lack of trust in and transparency
of high-quality black box metrics. In this work, we concern ourselves with the
last-named reason, and address the _explainability_ of such metrics.

In recent years, explainability has become a crucial research area in AI due
to its potential benefits for users, designers, and developers of AI systems
Samek et al. (2018); Vaughan and Wallach (2021).222 As of now, there is no
universally accepted definition of explainability in the AI community. In this
work, we adapt the definition by Barredo Arrieta et al. (2020), which we
discuss in §2.2.

For _users_ of the AI systems, explanations help them make more informed
decisions (especially in high-stake domains) Sachan et al. (2020);
Lertvittayakumjorn et al. (2021), better understand and hence gain trust of
the AI systems Pu and Chen (2006); Toreini et al. (2020), and even learn from
the AI systems to accomplish tasks more successfully Mac Aodha et al. (2018);
Lai et al. (2020). For AI system _designers and developers_ , explanations
allow them to identify the problems and weaknesses of the systems Krause et
al. (2016); Han et al. (2020), calibrate the confidence of the systems Zhang
et al. (2020b), and improve them accordingly Kulesza et al. (2015);
Lertvittayakumjorn and Toni (2021).

Explainability is particularly desirable for evaluation metrics. Sai et al.
(2022) suggest that explainable NLG metrics should focus on providing more
information than just a single score (such as fluency or adequacy).
Celikyilmaz et al. (2020) stress the need for explainable evaluation metrics
to spot system quality issues and to achieve a higher trust in the evaluation
of NLG systems. Explanations indeed play a vital role in building trust for
new evaluation metrics.333As an illustrating example, Moosavi et al. (2021)
distrust using the metric BERTScore Zhang et al. (2020a) applied to a novel
text generation task, as it assigns a score of 0.74 to a nonsensical output,
which could be taken as an unreasonably high value. While BERTScore may indeed
be unsuitable for their novel task, the score of 0.74 is meaningless here, as
evaluation metrics may have arbitrary ranges. In fact, BERTScore typically has
a particularly narrow range, so a score of 0.74 even for bad outputs may not
be surprising (BERTScore provides a scaling method that solves these issues to
some degree). Explainability techniques would be very helpful in preventing
such misunderstandings, e.g. by showing that the features BERTScore attends to
align with human judgement.  For instance, one potential reason that a metric
will likely be better accepted by the research community is if the
explanations of the scores align well with human reasoning and faithfully
reflect its internal decision process (see §2.2).

By contrast, if faithfulness is given and the explanations are counter-
intuitive, users and developers might lower their trust and be alerted to take
additional actions, such as trying to improve the metrics using insights from
the explanations or looking for alternative metrics that are more trustworthy.
Furthermore, explainable metrics can be used for other purposes: for example,
when a metric produces a low score for a given input, highlighted words (a
widely used method for explanation, see §4) in the input are natural
candidates for manual post-editing.

This concept paper aims at providing a systematic overview of the existing
efforts in explainable MT evaluation metrics and an outlook for promising
future research directions. The main focus of this work lies on explainable
evaluation metrics for MT, many of our observations and discussions can likely
be adapted to other NLG tasks, however.

We make the following contributions, outlined by the structure of our paper:

§2 Background & Terminology: We give an overview of concepts important for
explainable MT evaluation. We also highlight different goals and the audiences
that require them.

§4 Taxonomy: We provide a structured overview of previous efforts in
explainable MT evaluation. An overview is shown in Figure 4. The process of
selecting works for this taxonomy is described in §3 Literature Review &
Selection.

§5 Future Work: An analysis of underexplored research directions , and a
collection of exemplary methods from other NLP domains to illustrate potential
future paths. Here, we also discuss the usage of large language models (LLMs),
like ChatGPT OpenAI (2023b) and GPT4 OpenAI (2023a).

We provide an overview of Related Work in §6 and a Conclusion in §7.

With this work, we aim to solidify the field of explainable MT metrics and
provide guidance for researchers, in specific metric developers, who aim to
better understand and explain MT metrics, as well as users that want to employ
explainability techniques to explain metric outputs. In the long term, we
envision that our work will aid the development of improved MT metrics and
thereby help to improve the quality of machine translations. Potentially,
explanations can also aid other use cases such as translation selection and
semi-automatic labeling (see §2.3). We also hope that our work will more
generally inspire explainable NLG metric design.

##  2 Background & Terminology

In this section, we first introduce and relate definitions and dimensions of
MT metrics and explainability, which we use in the later parts of the paper.
Further, we collect goals and target audiences of explainable MT metrics.

###  2.1 Machine Translation Evaluation Metrics

MT metrics grade machine translated content, the hypothesis, based on ground
truth data. We differentiate these metrics along several dimensions. A summary
is shown in Table 1. We note that metrics can be categorized along many
dimensions (e.g. Sai et al. 2022; Celikyilmaz et al. 2020). Here, we focus on
a minimal specification that we will leverage later. Some parts of this
section refer to explainability (see definitions in §2.2).

Dimension |  Description
---|---
_Input type_ |  Whether source, reference translation or both are used as ground truth for comparison
_Granularity_ |  At which level a metric operates: Word-level, sentence-level, document-level
_Quality aspect_ |  What a metric measures: Adequacy, fluency, etc.
_Learning objective_ |  How a metric is induced: Regression, ranking, etc.
Table 1: A typology of categorizations for evaluation metrics.

Input type We call a metric reference-based if it requires one or multiple
human reference translations to compare with the hypothesis as ground truth,
which can be seen as a form of supervision (e.g. Papineni et al. 2002; Zhao et
al. 2019; Rei et al. 2022a). Reference-free metrics do not require a reference
translation to grade the hypothesis (e.g. Zhao et al. 2020; Ranasinghe et al.
2020b; Belouadi and Eger 2023). Instead, they directly compare the source to
the hypothesis. In the literature, reference-free MT evaluation is sometimes
also referred to as “reference-less” (e.g. Mathur et al. 2020) or “quality
estimation” (e.g. Zerva et al. 2022). This dimension is important for
explainable MT evaluation, as explainability techniques might need to consider
which resources are available.

Granularity Translation quality can be evaluated at different levels of
granularity: word-level, sentence-level and document-level. The majority of
metrics for MT return a single sentence-level score for each input sentence
(e.g. Zhao et al. 2019; Zhang et al. 2020a). Beyond individual sentences, a
metric may also score whole documents (multiple sentences) (Jiang et al. 2022;
Zhao et al. 2023). In the MT community, metrics that evaluate translations at
the word-level (for example, whether individual words are correct or not) are
also common (Turchi et al. 2014; Shenoy et al. 2021). Recently, there has been
a tendency to compare metrics to human scores derived from fine-grained error
annotations — multidimensional quality metrics (MQM) (Lommel et al. 2014) — as
they tend to correspond better to human professional translators (Freitag et
al. 2021a). Metrics of higher granularity (e.g. word-level) provide more
explanatory value than those of lower granularity, as they provide more
details on which parts of the input might be translated incorrectly (e.g.
Leiter 2021; Fomicheva et al. 2021, 2022). Most techniques we describe in §4
target the explainability of sentence-level metrics.

Quality aspect This refers to the properties a metric evaluates and measures,
for example, human assigned adequacy (via direct assessment (Graham et al.
2016) obtained from crowd-workers), fluency, or other aspects (such as
relevance, informativeness, or correctness, mostly in other NLG fields) (e.g.
Sai et al. 2022; Celikyilmaz et al. 2020). We use this dimension in §4, as
metrics that report multiple quality aspects provide more information on
specific translations, i.e., they have more explanatory value for potential
users.

Learning objective Yuan et al. (2021) describe a further distinction based on
the task a metric is designed to solve (or _how_ a metric is implemented) to
achieve high correlation with human judgments. They identify the three tasks
below.444Chen and Eger (2023) argue for metrics induced from natural language
inference (NLI) as a conceptual framework.

  * •

Unsupervised Matching: They place all metrics that match hypothesis and
reference tokens in this dimension, e.g. BLEU (Papineni et al. 2002) and
BERTScore (Zhang et al. 2020a).

  * •

Supervised Regression/Ranking: These models use supervised regression to train
a metric to predict continuous values that directly correlate with human
judgments from hypothesis and ground truth tokens. Alternatively, a metric may
be trained to rank hypothesis sentences. For example, Rei et al. (2020)
propose a ranking setup in which they minimize a loss that becomes smaller the
greater the distance between embedding representations of the two sentences
become. Especially in the MT community, metrics trained on human scores (e.g.
COMET; Rei et al. 2022a) have become more and more dominant, while Belouadi
and Eger (2023) argue that unsupervised metrics are more widely applicable.

  * •

Text Generation: Some metrics use the probability that a sentence (e.g., a
translation) is created by a text generation system, given an input sentence
(e.g., a reference sentence), as sentence-level score. Yuan et al. (2021)
place the metric PRISM (Thompson and Post 2020) and their own metric BARTScore
in this setup.

The distinction of learning objectives is important for explainable MT
evaluation, as the applicability of some explainers depends on the objective.

MT metrics also have multiple use cases; see Figure 1 (in the following,
preceding numbers correspond to this figure). (1) One use case is to rate a
single translated text, for example, in a scenario where a human user wants to
check their own human555Note, however, that humans might make different types
of translation errors (Specia et al. 2018). Therefore, metric performance
first needs to be evaluated for this use case. translation or a translation
they received by an MT model before using it in a downstream task (e.g.
Murgolo et al. 2022; Zouhar et al. 2023). (2) The standard use case is to rate
a large number of translations to compare MT systems (e.g. Freitag et al.
2022). (3) Another use case is to employ metrics in the training process of MT
models, e.g. using reinforcement learning (e.g. Wu et al. 2018) or optimizing
by computing derivatives with Gumbel-softmax (e.g. Jauregi Unanue et al.
2021). (4) The fourth use case is to use the metrics in the decoding process
of machine translation systems for reranking or with minimum Bayes risk
decoding Fernandes et al. (2022a).

The use cases can have different audiences and explainability might play a
different role to them (see 2.2). For example, anyone might be a user that
wants to rate a translation, but usually only domain experts want to rate a
translation model.

Figure 1: An overview of use-cases for MT metrics. The standard use case is to
rate a translation model. In number 4, many candidate translations are
produced for a single source. One of them is chosen as the main translation,
via the metric.

###  2.2 Explainability

In this section, we (1) define explainability in the scope of this work, (2)
discuss the definitions and roles of faithfulness and plausibility for
explainable MT metrics and (3) contrast explainability with the terms
robustness and fairness. Then, we (4) discuss the properties we use to
structure and describe the taxonomy of prior works on explainable MT metrics
(see §4), namely explanation level, explanation type, model access and
audiences. Based on (1), we decide on the scope of papers we include in the
taxonomy in §3.

Explainability & interpretability Prior work has noted that the terms
interpretability and explainability vary in definition and are sometimes used
ambiguously throughout the literature (Barredo Arrieta et al. 2020; Jacovi and
Goldberg 2020; Vilone and Longo 2021). In this paper, we follow the notion of
_passive interpretability_ and _active explainability_ (for example, Barredo
Arrieta et al. 2020). In particular, we adapt the definition of Barredo
Arrieta et al. (2020) as follows:

> An MT metric is explainable if itself or some external instance, an
> explainer, can actively provide explanations that make its decision process
> or its output clearer to a certain audience.

This definition varies from Barredo Arrieta et al. (2020) in two aspects: (1)
It includes external explainers (also called post-hoc methods) and (2) it
includes explanations of the metric’s output. We note that (2) is different
from explaining a metric, as output explanations may not provide insights into
how a model works. Still, we include (2) in our explainability definition for
two reasons. One is that there have been two shared tasks and other existing
works on metric explainability that consider the plausibility aspect of
explanations only (see the following paragraph on faithfulness and
plausibility) (e.g. Fomicheva et al. 2020, 2021; Zerva et al. 2022; Xu et al.
2023). Thereby, explanations these works consider might be explanations of the
output that do not actually describe model internals. We want to accommodate
these works that do not check faithfulness in our definition. The other reason
is that we see use cases of output explanations of strong metrics, i.e.,
plausible-only explanations (see the following paragraph on faithfulness and
plausibility).

In contrast, a metric is interpretable if it can be passively understood by
humans (for example, decision trees and k-nearest neighbors) (Barredo Arrieta
et al. 2020). Modern MT metrics are mostly based on embeddings and black box
language models, making them non-interpretable. The papers we categorize in
this work generate explanations for MT metrics and their outputs, i.e. they
consider explainability.

CounterfactualsExplainability (&
Interpretability)RobustnessFairnessResponsible
AIfineevaluationapplicationcoarsefineevaluationapplicationcoarse

Figure 2: Venn-diagram that highlights the relationship of our explainability
definition to related terms in the field of responsible AI. Evaluation of the
properties robustness and fairness contributes to explainability, as the
results also describe how a model is generally behaving. The more fine-grained
these results are, the higher the explainability. The borders between
explainability and robustness/fairness are not fixed. To explain, coarse
evaluation methods also can provide explanatory value and, in some cases,
explainability can aid the actual application of making models robust and
fair. One common component in evaluation methods from these domains are
counterfactuals (see §2.2 for their description). The graphic focuses on the
relations of explainability. Relationships between robustness and fairness are
ignored.

Faithfulness & plausibility An explanation is faithful if it accurately
represents the reasoning process underlying a model’s prediction(s) (Jacovi
and Goldberg 2020). In contrast, an explanation is plausible if it convinces
its audience that a model’s prediction is correct (Jacovi and Goldberg 2021),
i.e., if it aligns with human judgment. Both properties are not necessarily
dependent on each other (Jacovi and Goldberg 2020). For example, an
explanation of an MT metric score could make sense to a human without
reflecting the metrics internal details and vice-versa. A degree of
faithfulness is indispensable to all explainability works that actually want
to reason about a model’s decision processes. Plausibility, on the other hand,
might convince explainees of decision processes that do not reflect the
model’s internals correctly, or incorrect model predictions. As such,
optimizing for plausible explanations might be unethical and lead to deceiving
outcomes (Jacovi and Goldberg 2021; Jin et al. 2023). However, in our
taxonomy, we find that most current works on explainable MT metrics do not
consider faithfulness and only evaluate plausibility (see §4), often leaving
unknown whether and to which degree the model’s internal process is accurately
described by explanations. While this is a decisive current shortcoming, we
argue that explanations that may be only plausible can still benefit certain
audiences and use cases of MT metrics that do not target understanding the
metric: (1) We assume that current high-quality metrics already work well to
judge translation quality in many settings (Freitag et al. 2022; Zerva et al.
2022). Explainers that are proven to have a high plausibility to human
experts, as for example measured by Fomicheva et al. (2021); Zerva et al.
(2022), will likely provide strong reasoning about the translation quality
when provided with a correct score by such metrics. We caution that this
process will have two points of failure: metric and explainer, hence the
overall rate of failures will be higher than for each of them individually.
Still, we predict that this process will work reasonably well to foster the
goals of metric accessibility (see §2.3), as long as users are aware of
possible failures (see future work §5), similar to the problem of
hallucinations in LLMs (Ji et al. 2023b). (2) Language experts should be able
to discern incorrect explanations. For them, explanations of quality scores
(produced by high quality metrics) that strive to be plausible could provide
means to faster evaluating and (if necessary) correcting translations, as
encompassed by the goals of metric accessibility and semi-automatic labeling
(see §2.3). (3) LLM’s have been shown to provide better results when being
prompted to explain them (Wei et al. 2022). Here, more plausible explanations
might lead to better scores and vice-versa (see §5). (4) Similar to (3),
language models sometimes benefit from multitask learning (Radford et al.
2019), so employing automatically generated explanations might assist training
better metrics. Note that (1) and (2) correspond to the first metric use case
in Figure 1. To summarize, plausible but not faithful explanations could still
be useful either to human users or intrinsically to a metric itself in several
scenarios.

Fairness & robustness In Figure 2, we show the relationship of our definition
of explainability to robustness and fairness. Robustness measures how well a
model (here, a metric) can address execution errors, erroneous and malicious
inputs and unseen data (e.g Li et al. 2023). In contrast, Li et al. (2023)
define fairness as the goal that a model should not have biases that result in
unfair treatment for certain groups of people. As umbrella term for these
concepts, we use responsible AI Barredo Arrieta et al. (2020).666There are
many related and partly conflicting definitions and terms in explainable AI
research (e.g. Vilone and Longo 2021) and the given definitions are not yet
firmly established in the literature. For example, Sharma et al. (2020) define
explainability, robustness and fairness as exclusive properties. The main
focus of our work is to build a taxonomy of methods for explainable MT metrics
and the chosen definitions allow us to reasonably relate existing work.
Barredo Arrieta et al. (2020) define responsible AI as a concept that
encourages the systematic adoption of several AI principles, while also
setting a focus on accountability and privacy. Methods that test for
robustness and fairness can all be grouped into the given explainability
definition, as their fulfillment requires explanations that will also help
certain audiences to understand a model’s behavior better Barredo Arrieta et
al. (2020).777Barredo Arrieta et al. (2020) group robustness under the term
confidence. In the MT metrics literature, this overlap in terminology is
apparent for techniques that check how robust metrics are to input
perturbations. Here, some works frame these as explainability techniques (e.g.
Sai et al. 2021; Karpinska et al. 2022), while others frame them as robustness
tests (e.g. He et al. 2022; Chen and Eger 2023); see §4. In Figure 2, we only
let robustness and fairness half overlap with explainability to highlight that
the explanatory value increases with fine-grained explanations. E.g.,
robustness could be measured coarse-grained with a single score, such as the
minimal number of adversarial perturbations required to successfully attack a
metric, or fine-grained, by using predefined perturbation templates (e.g. Sai
et al. 2021) or attackers that will attack with perturbations of specific
properties (e.g. Chen and Eger 2023). While a single score can also explain
whether metric behavior is robust, susceptibility scores to various properties
allow to assess metrics in greater detail. The same is true for fairness (Sun
et al. 2022). Another reason that we do not fully include robustness and
fairness in the explainability definition is that explainability results from
testing them, but vice-versa robustness and fairness are usually not given
just by having an explainable model.

We note that common explanation types in the evaluation of robustness and
fairness are counterfactual explanations, i.e., minimal perturbed input
samples that change the output of a model to demonstrate its behavior Wachter
et al. (2017); Freiesleben (2021). For fairness evaluation, counterfactuals
from domains that carry a risk of being biased are used Sharma et al. (2020);
Sun et al. (2022). For robustness, existing works check whether metric scores
change as expected, when presented with counterfactuals in contrast to the
original hypotheses (e.g. Sai et al. 2021; Chen and Eger 2023); see §4 for
more details. Some works frame these counterfactuals as adversarial attacks
(e.g. Sharma et al. 2020; Chen and Eger 2023), others as diagnostic tests(ets)
(e.g. Karpinska et al. 2022) or challenge sets (e.g. Freitag et al. 2022).

Explanation level Explainability techniques can be distinguished into
_decision-
understanding_ and _model-understanding_ (e.g. Gehrmann et al. 2020; Holzinger
et al. 2022). Decision-understanding methods are described as explaining the
decision process for a specific model output with respect to a specific input
while model-understanding methods are described as explaining general
properties of a model that steer its decisions. Often, decision-understanding
is also referred to as _local_ and model-understanding as _global_ (e.g.
Lipton 2016; Guidotti et al. 2018; Doshi-Velez and Kim 2017; Danilevsky et al.
2020).

Explanation type Further, we distinguish explanation types, i.e., the output
format of the explanations (e.g. Wiegreffe and Marasovic 2021). One example
are natural language explanations that describe the model output or its
internal decision process with free text. In the case of MT metrics, this
could be a sentence like “The metric returned a score of 0.6 for the
translation, as it correctly captures all details of the source sentence but
has severe grammatical errors at positions […]”. There are many possible
explanation types (e.g. Barredo Arrieta et al. 2020; Wiegreffe and Pinter
2019), such as, feature importance explanations or counterfactuals. For our
taxonomy, we select those that occur in our target papers (see §3). The
definition of each explanation type is paired with the description of methods
in §4.

Model Access Most taxonomies of explainability techniques consider the
required level of model access (e.g. Barredo Arrieta et al. 2020; Linardatos
et al. 2021; Madsen et al. 2022). Here, techniques that require no access
(that can work with any black box model) are called model-agnostic. Techniques
that require access to model parameters are called model-specific, as only
specific model architectures satisfy the requirements of the method. Due to
the prevalent role of transformer architectures, many modern evaluation
metrics use embeddings and attention mechanisms and allow for gradient
computations. This allows to apply many techniques that are specific to these
architectures to such metrics. A special type of model-specific explanations
are those that are given by a model (here a metric) itself. We call these
metrics “explainable by design”, as they are designed in a way that provides
more details on their explanations or decision process. Examples for MT
metrics in this category are unsupervised matching metrics like BERTScore
Zhang et al. (2020a) and MoverScore Zhao et al. (2019), as they compute token-
level similarity scores that can be used as explanations of the sentence-level
score Leiter (2021); Fomicheva et al. (2021). Finally, there are also
interpretable models which are easy to understand for humans such as Naive
Bayes and decision trees Freitas (2014); Arya et al. (2019). We can normally
obtain decision-understanding explanations of these models at the same time as
predictions (for example, the corresponding path in the decision tree), while
the models can also be considered explanations of themselves (for example, the
trained decision tree itself). We do not use the dimension of interpretable
models in our taxonomy (see §4) as none of the state-of-the-art metrics falls
into this category. Model-agnostic techniques require least model access,
model-specific techniques, which use the gradient, embeddings or attention
weights require more access and models that are explainable by design require
access to the full model to generate explanations.888Note that besides
interpretable models, none of the access levels inherently leads to faithful
explanations, i.e. explanations that correctly describe the internal processes
of a model. Hence, this property needs to be considered separately (e.g.
Jacovi and Goldberg 2020).

Audiences Prior works in explainability point out that different audiences
require different types of explanations (e.g. Barredo Arrieta et al. 2020;
Sharma et al. 2020). For example, non-experts might have difficulties
understanding explanations given in the domain language of experts. Barredo
Arrieta et al. (2020) distinguish model users, affected users, developers,
regulatory entities and managers. Users of MT metrics are usually domain
experts in MT that want to evaluate or improve MT systems, i.e. MT developers.
Another group of users might be non-experts that want to use the models to
check their own translations or machine generated translations. Affected users
might be the ones that receive translations that were graded by a metric or
generated by a model that was supervised or tested with the metric. For
example, Fernandes et al. (2022a) employ MT metrics in the decoding process of
MT models. We consider users of those MT models as affected users. Developers
are the designers of MT metrics. Regulatory entities and managers have an
interest in enforcing guidelines. We describe the relation of the audiences
towards goals of explainable MT metrics in §2.3 and associate audiences with
explanation types in §4.

###  2.3 Goals of Explainable MT Metrics

In this section, we discuss goals that require specific focus for explainable
MT evaluation. We orient ourselves on common goals proposed by Lipton (2016);
Barredo Arrieta et al. (2020); Jacovi and Goldberg (2021) and identify four
specific ones that match the literature we identify in §3 and use cases that
might commonly occur in our field of research (see Figure 3). We follow with a
short description of each of them below.

Figure 3: Goals of explainable MT evaluation. Each goal has audiences, which
might be interested in its fulfillment; these are indicated with “A:”.
Further, each goal follows intentions, which are denoted with
$\shortrightarrow$.

  * •

Goal 1: Diagnose and improve metrics. By explaining why a metric predicted a
certain score for a machine translation, where humans would assign a different
score, developers might understand its weaknesses (e.g. Kaster et al. 2021;
Sai et al. 2021). This can enable architectural changes or the selection of
different data sets, leading to metric improvement. Likewise, explaining the
whole metric can unveil whether it follows desired general properties or might
otherwise be led astray by carefully crafted adversarial inputs. Goal 1 is
especially important for the audience metric developers, as they need to
understand the shortcomings to improve on them.

  * •

Goal 2: Make metrics more expressive and accessible. A metric that assigns a
score on a sentence level is difficult to understand for non-experts, e.g.,
non-native speakers. If a metric provides further information, such as which
words it considers incorrect, it may become more accessible and thus foster
trust (which may lead to more widespread adoption of the metric). Exemplary
works in this direction are Fomicheva et al. (2021); Bao et al. (2023).
Accessible metrics might also help with use cases like translation error
correction in the downstream tasks of language learning or human translation
services (e.g. Zouhar et al. 2023). In these latter two use cases,
accessibility might not require faithfulness of the explanation (see §2.2).
Goal 2 is important for the audience of metric users. These can be either non-
experts that want to understand specific metric outputs or developers of MT
systems that want to understand if a metric is a good fit for their tasks (see
Figure 1).

  * •

Goal 3: Support Semi-Automatic labeling. Fine-grained annotations like word-
level translation error labels (e.g. Fomicheva et al. 2021) are especially
difficult to obtain. Obtaining automatic explanations to aid human annotators
could boost their efficiency tremendously (e.g. Desmond et al. 2021). To our
best knowledge, none of the current works has applied explainers in these
settings for metrics. Semi-automatic labeling could also help with post-
editing of translations (e.g. Specia and Farzindar 2010) to guide how
sentences should be adapted. Goal 3 is important for metric developers that
want to create such word-level translation error data sets and metric users
that want to correct translations.

  * •

Goal 4: Checking for social biases. Learned metrics might be biased towards
certain types of texts used during training (e.g. Sun et al. 2022). Biases
could be detected by observing explanations where sensitive attributes (for
example, gender, political or racial aspects) are flagged to be important
(Lipton 2016). For example, explanations could show that a metric considers
male names more important for a translation than female names in the same
scenario. Goal 4 can be important for all audiences. Affected users might be
treated unfairly if biases occur in a metric. Metric users, metric developers,
regulatory entities and managers might want to prevent this from happening to
conform with given regulations.

##  3 Literature Review & Selection

To build the taxonomy in §4, we survey literature which either explicitly
state that they address the explainability of MT metrics or propose techniques
that make MT metrics or their outputs clearer to understand by certain
audiences (techniques that provide explanations). We restrict our search and
taxonomy to works that consider neural network based MT metrics. We conducted
an initial literature search by selecting 11 “seed papers”. Then, we leveraged
the API of Semantic Scholar999https://www.semanticscholar.org/ to query new
papers citing the work of the seed papers and papers citing those papers
(citation tree at depth 2). This amounted to 383 papers. Next, we filtered the
papers by keywords, leaving 177 papers. Then, we manually annotated whether
their contents fit our definitions. Thereby, we identified 12 papers that
consider the explainability of MT metrics. In later stages, we progressively
added further work released after the initial search and related works that we
identified from other sources. In total, we describe 29 papers, some of which
are not shown in Figure 4 due to space constraints.

Following §2.1, we consider higher granularity scores (e.g. word-level) as
explanations for lower granularity metrics (e.g. sentence-level). However,
without requiring faithfulness this definition allows to view any word-level
metric as explainers for any sentence-level metric. Thereby, it would even
include cases in which the word-level scores are determined completely
independent from the sentence-level, i.e., cases where the explainer would not
be faithful. Due to the high number of existing word-level approaches, for
this explanation type, we only consider works that explicitly employ the word-
level scores for explainability.

Likewise, following §2.1, metrics that report multiple quality aspects such as
adequacy or fluency provide more explanatory value on why an explanation is
good or bad than metrics that return a single score. For the quality aspect
explanation type, we make an exemplary selection of techniques derived from
recent metrics that leverage generation probabilities (e.g. Thompson and Post
2020; Yuan et al. 2021).

We also include a few non-MT works into our taxonomy, which show how some
aspects that are currently not addressed by the MT related works might be
tackled.

less information more
information${}^{i}_{a}$qualityaspects${}^{i}_{a}$${}^{i}_{a}$featureimportance${}^{i}_{a}$${}^{i}_{a}$fine-
grainederrors${}^{i}_{a}$${}^{i}_{a}$perturbationrobustness${}^{i}_{a}$${}^{i}_{a}$linguisticproperties${}^{i}_{a}$post-
hoc ${}^{i}_{a}$black-
box${}^{i}_{a}$${}^{i}_{a}$gradient${}^{i}_{a}$${}^{i}_{a}$attentionweights${}^{i}_{a}$${}^{i}_{a}$embeddings${}^{i}_{a}$intrinsic
${}^{i}_{a}$model specific${}^{i}_{a}$${}^{i}_{a}$(Thompson and Post
2020)(Yuan et al. 2021)(Kamal Eddine et al. 2023)(Fu et al.
2023)${}^{i}_{a}$${}^{i}_{a}$(Kaster et al.
2021)E${}^{i}_{a}$${}^{i}_{a}$(Golovneva et al. 2023)N(Opitz and Frank
2022)N${}^{i}_{a}$${}^{i}_{a}$(Rei et al. 2022b)E(Rei et al.
2023)E${}^{i}_{a}$${}^{i}_{a}$(Treviso et al.
2021)E${}^{i}_{a}$${}^{i}_{a}$(Fernandes et al. 2022b)E(Kabir and Carpuat
2021)E(Rubino et al. 2021)E(Wang et al. 2022)E${}^{i}_{a}$${}^{i}_{a}$(Leiter
2021)E(Tao et al. 2022)E(Azadi et al. 2022)E${}^{i}_{a}$${}^{i}_{a}$(Eksi et
al. 2021)E${}^{i}_{a}$${}^{i}_{a}$(Fomicheva et al.
2022)E${}^{i}_{a}$${}^{i}_{a}$(Leiter et al. 2022b)${}^{i}_{a}$${}^{i}_{a}$(Lu
et al. 2022, 2023)(Xu et al. 2023)E, …${}^{i}_{a}$${}^{i}_{a}$(Sai et al.
2022)E(Karpinska et al. 2022)E(He et al. 2022)R(Sun et al. 2022)F(Chen and
Eger 2023)R…${}^{i}_{a}$${}^{i}_{a}$(Kaster et al.
2021)E${}^{i}_{a}$${}^{i}_{a}$(Opitz and Frank 2022)N${}^{i}_{a}$decision-
understanding model-understanding

Figure 4: Overview of papers addressing the explainabilitiy of machine
translation evaluation. The graphic is adapted from a survey on post-hoc
methods for explainable NLP by Madsen et al. (2022). The rows show the types
of explanations returned by the respective methods. They are ordered from
decision-understanding to model-understanding. Decision-understanding
techniques explain specific outputs of a metric, while model-understanding
techniques describe general properties. The columns show the required level of
model access that each explainability technique needs. Fields, where boxes
overlap are colored in a darker grey. Note that most work listed under feature
importance was developed as part of the Eval4NLP21 (Fomicheva et al. 2021) and
the WMT22 QE (Zerva et al. 2022) shared tasks. N indicates that a technique is
not directly from the MT domain. R indicates that a technique was proposed to
tackle robustness. F indicates that the technique was proposed to explore
fairness aspects. E is set when the authors themselves perceived that their
method tackles explainability.

Quality AspectsAdequacy: 0.7Fluency: 1Feature
Importance$0.5$$0.1$$0.5$$\shortrightarrow$
“Thingsbecamecomplicated”$0.5$$0.4$$0.1$$0.5$$\shortrightarrow$ “Die
Dingebekamenkompliziert”Fine Grained Errors“Things
$<$sense$>$became$<$/sense$>$ complicated”“Die Dinge
$<$sense$>$bekamen$<$/sense$>$ kompliziert”Perturbation RobustnessNumeric: “3
Dinge bekamen kompliziert”$\shortrightarrow$lower scoreOrder: “kompliziert die
Dinge bekamen”$\shortrightarrow$lower score…Linguistic PropertiesExample
Data:Ground Truth: “Things became complicated”Hypothesis: “Die Dinge bekamen
kompliziert”Score: $0.8$Decision-UnderstandingModel-UnderstandingExplanation
Types

Figure 5: Explanation types shown in §4. For each type we provide a
hypothetical example. For this we assume the exemplary translation that is
shown in the top-left box. Here, the polysemic word ”became” is translated
with the wrong word sense ”to receive” instead of ”to get/to develop into”.
The correct translation is ”wurden”. The example for perturbation robustness,
perturbs the hypothesis sentence.

##  4 Taxonomy

Based on the background described in the previous sections and the literature
search we conducted, we create a taxonomy of prior works that consider the
explainability of MT metrics. This taxonomy is shown in Figure 4. The taxonomy
contains five explanation types based on the selected papers’ contents:
quality aspects, feature importance, fine-grained errors, perturbation
robustness and linguistic properties. We structure this section based on these
explanation types. Figure 5 shows an example for each explanation type. For
each type, we describe the techniques applied by the previous works and how
they relate to each other. We begin with explanation types that explain
specific samples and move to techniques that explain metric behavior in
general.

Quality aspects (Row 1 in Figure 4) This category comprises works that present
explanations of the metric output by producing separate scores for different
aspects of the translation. Most methods that we list develop generation-based
metrics that are explainable by design (“model specific” in Figure 4). This
means, they do not use an external explainer that could be applied to other
metrics. BARTScore (Yuan et al. 2021) uses the autoregressive generation model
BART (Lewis et al. 2020) to predict the average word generation probability of
a sentence $B=(b_{1},...,b_{m})$, given a sentence $A=(a_{1},...,a_{n})$:

$BARTScore(A\longrightarrow B)=\sum_{t=1}^{m}\log p(b_{t}|b_{<t},A,\theta)$

This formula sums over the log-probability of token $b_{t}$ being generated by
BART with parameters $\theta$ given all previous tokens of $B$ and all tokens
of $A$. Depending on whether A is the ground truth sentence and B the
hypothesis (or vice-versa), Yuan et al. (2021) state that different aspects of
the translation are evaluated. For example, they associate the generation
direction of A:source $\longrightarrow$ B:hypothesis with the properties
coherence and fluency. This idea of using the generation probabilities in
multiple directions was first introduced in PRISM (Thompson and Post 2020),
but Yuan et al. (2021) formalized the connection of the directions to known
quality aspects. DATScore (Kamal Eddine et al. 2023) extends BARTScore by
using a multilingual model and further generation directions (additionally
using the source sentence). The metric GPTScore (Fu et al. 2023) provides a
suite of prompts for language models that emphasize specific aspects when
requesting a translation or paraphrase. Then, similar to BARTScore, the
generation probabilities are averaged for each word. For example, they use the
prompt “Rewrite the following text to make it more grammatical and well-
written: [reference or hypothesis] In other words,” to generate a paraphrase
whose probabilities they use for a fluency score. The example specifies
reference/hypothesis to describe that the metric checks either the generation
probability of the hypothesis given the reference, or vice-versa.

Another way to gather fine-grained scores is to apply suites of metrics (each
of which considers different aspects), like ROSCOE Golovneva et al. (2023), an
embedding based metric suite, which has been proposed to grade step-by-step
reasoning processes (column “embeddings” in Figure 4).

Besides adding explanatory value per se, quality aspect explanations have two
more potential use cases for explainable metrics: (1) separate quality scores
could be aggregated, to compute single, decomposable sentence-level scores and
(2) scores could be aggregated over data sets to check which aspects a metric
favors the most. For example, Kaster et al. (2021); Opitz and Frank (2022)
also assign quality aspect scores, but mostly use them for model
understanding; see linguistic properties explanations for more details.

In the example presented in Figure 5, we report fluency and adequacy as
quality aspects. We assign a fluency score of 1 on a scale from 0 to 1, as the
translation is fluent and an adequacy of 0.7 as the translation of “became” is
incorrect.

Goals: Quality aspect approaches could be used for metric accessibility and
semi-automatic labeling. The usage for metric diagnosis is limited, as the
approaches are often based on models that are explainable by design and might
not be faithful (the models generate their own explanations). The current
approaches cannot be used to detect social biases. Audiences: The quality
aspects fluency and adequacy follow simple definitions, such that these
metrics could be used by non-expert and expert users. Limitations: The
generation directions might not fully match the quality aspects. Also, the
usage of prompting leaves it to another black box setting whether the model
picks up the desired properties or not. Lastly, the explanatory value of
quality aspect approaches per se is rather small, as they only allow to
reason, “the rated translation is good because of aspect x” and to guess about
the model’s reasoning. As none of the current works101010Kaster et al. (2021);
Opitz and Frank (2022) are considered at a later point. specifically targets
explainability, they do not consider faithfulness. Plausibility is
automatically considered in standard evaluation schemes with human judgments.

Feature importance (Row 2 in Figure 4) Feature importance techniques assign
scores to all features in the input of a metric (here the input tokens) that
express their importance to the output of the metric.111111Other names include
relevance scores or attribution scores. Sometimes the scores are used to
highlight important words. Fomicheva et al. (2022) note that high feature
importance scores should correspond to translation errors, as humans pay most
attention to errors when rating a translation. They build on the idea that
when humans evaluate translations, they often focus on the errors that they
can identify on a word- or phrase-level (Freitag et al. 2021a). Then,
Fomicheva et al. (2022) evaluate how well feature importance is correlated
with human word-level error annotations, i.e., the plausibility of the
explanations.

We note that, depending on the explainability method, these scores might be
inverted, following the interpretation “errors are not important for a metric
to achieve a high score”.121212Most gradient and attention based approaches
return high scores for errors (e.g. Treviso et al. 2021; Rubino et al. 2021),
for embedding-based approaches it depends on whether cosine-similarity or
-distance is used (e.g. Leiter 2021; Tao et al. 2022), for attribution based
techniques using LIME (Ribeiro et al. 2016) or SHAP (Lundberg and Lee 2017),
low scores are returned for errors (e.g., as part of the baseline
implementations in Eval4NLP21:
https://github.com/eval4nlp/SharedTask2021/tree/main/baselines). Two shared
tasks for the explainabiltiy of reference-free metrics have originated from
the setting in Fomicheva et al. (2022) so far (Fomicheva et al. 2021; Zerva et
al. 2022). As the respective papers already give an overview of the
participating methods, we will only briefly describe the methods and set our
focus to describe the model access they require. We describe the model access
levels of Figure 4 from left to right. Approaches that work directly on black
box models are explored in Fomicheva et al. (2022); Eksi et al. (2021);
Treviso et al. (2021). They leverage model agnostic explainers such as LIME
(Ribeiro et al. 2016) and SHAP (Lundberg and Lee 2017). These methods check
how a metric’s sentence level score changes when the input sentence is
perturbed at selected positions. While the results correlate with the human
annotations to some degree, techniques that use model access performed better
in the shared tasks. Gradient based methods are explored by Fomicheva et al.
(2022); Eksi et al. (2021); Treviso et al. (2021); Rei et al. (2022a). These
papers use methods like Integrated Gradients (Sundararajan et al. 2017) to
determine the effect each input token has on the output. Attention values
perform especially good for Treviso et al. (2021); Rei et al. (2022b), leading
to winning techniques for most language pairs in both shared tasks, i.e.,
producing the most plausible explanations. In specific, Treviso et al. (2021)
succeed by scaling attention weights by the norm of value vectors and
filtering for attention heads that have the highest scores in detecting
errors. Rei et al. (2022b) build on their approach and (1) instead scale
attention weights with the l2-norm of the gradient of value vectors and (2)
designing an ensemble method for attention heads. In their final submission,
they ensemble the best performing attention heads over all layers. Word-level
contextualized embeddings are used by Leiter (2021); Tao et al. (2022); Azadi
et al. (2022). Specifically, they leverage the word-level embedding distances
that are inherently computed in token-matching metrics (see §2.1) and use them
as importance scores. For model-specific works, Rubino et al. (2021) jointly
fine-tune their metrics on the word-level and sentence level using synthetic
data, to directly output the computed word-level scores.131313Rubino et al.
(2021) won the constrained track of Eval4NLP21, which did not allow for word-
level finetuning. Kabir and Carpuat (2021) use LIME with Transquest
(Ranasinghe et al. 2020a) and merge the word-level importance scores with
additional word-level scores obtained from a pure word-level model. After the
shared tasks, Fernandes et al. (2022b) enhance explainability techniques (in
case of MT they use a gradient technique) by adding learnable parameters and
employing a student teacher mechanism to improve the correlation to human
word-level scores. Rei et al. (2023) extend the work of Rei et al. (2022b) by
using reference-based metrics (e.g. COMET) and importance scores of the
references. They construct a data set from the WMT21 MQM annotations Freitag
et al. (2021b) by assuming that feature attribution scores link to annotated
error spans. Evaluations on this data set show that the winning explainability
techniques of the shared task perform best and that references can improve the
plausibility. Notably, they also show that explanations can help to detect
critical errors in translations. Based on this, they employ the explanations
for metric diagnosis, to test whether their metrics are more prone to ignoring
certain types of critical errors. In their limitations, they note that the
explainabiltiy techniques they use have shown to be faithful in some
scenarios, but also note that they did not specifically test whether they are
faithful for their architecture. Finally, we note that many more metrics can
provide feature-importance like explanations by design. Similar to the base
metrics for Leiter (2021); Tao et al. (2022); Azadi et al. (2022), most
unsupervised matching metrics could be used to determine importance scores
based on word- or embedding-similarities. Further, the per-word generation
probabilities in text generation metrics like PRISM or BARTScore can also be
interpreted as feature importance (they can also be interpreted as the
confidence of the generation models; Fomicheva et al. 2020).

In the example presented in Figure 5, high feature importance scores are
assigned to words that were translated correctly, i.e. “Things/complicated”
and lower scores indicate words that are wrong.

Leiter et al. (2022b) take a different approach on using feature importance
techniques for the goal of metric improvement. Instead of using the feature
importance scores in a task that might involve human explainees, the authors
aggregate and directly integrate the feature importance scores into metric
scores. This follows the intuition that the explanations carry additional
information that can be beneficial to the sentence-level correlation of a
metric with human judgments.

Goals: The discussed feature importance approaches are tested for
plausibility, so strong approaches could be used for metric
accessibility/improvement and semi-automatic labeling. The usage for metric
diagnosis is currently limited, as only samples are explained, and approaches
have not been tested for faithfulness. The current approaches have not been
used to detect social biases. Audiences: The relation of low/high scores and
translation errors is intuitive, such that these techniques could be used by
non-expert and expert users. Metric developers could use the methods to create
new data sets or to directly incorporate them into metrics Leiter et al.
(2022b). Limitations: For all approaches, it is not specifically tested
whether the explanations are faithful.141414In different possible aspects
(Jacovi and Goldberg 2020) Notably, Rei et al. (2023) consider that
faithfulness is probably given in their case but note the missing tests as
limitation. Second, certain translation errors cannot be easily captured by
highlighting specific words. For example, the word-level scores cannot
represent cases where the MT fails to explicitly express a grammatical feature
that is omitted in the source language but is required to be explicit in the
target language (for example, the use of articles when translating from
Russian into German). Third, the explanation type does not provide
correspondence between highlighted error words in the source and target
language.

Fine-grained errors (Row 3 in Figure 4) Some recent works detect errors on a
word-level and leverage their number and type to compute a sentence-level
score (Lu et al. 2022, 2023; Bao et al. 2023; Xu et al. 2023). This follows
the ideas of the MQM annotation scheme (Lommel et al. 2014; Freitag et al.
2021a) in which human experts evaluate translations in a similar manner.
Freitag et al. (2021a) have shown that this annotation scheme improves over
previous annotation methods such as direct assessments (Graham et al. 2015).
While the main goal of these works is to provide sentence-level scores, they
are also explainable-by-design and the generated error-labels carry
explanatory value (model-specific in Figure 4). In specific, BARTScore++ (Lu
et al. 2022) distinguishes explicit errors that are easy to detect and
implicit errors. They detect the explicit errors based on BART generation
probabilities. Then they generate a sentence-level score based on an
additional improved hypothesis sentence in multiple BARTScore calculations. Lu
et al. (2023) also combine fine-grained errors into a score. They first
request ChatGPT to generate explanations based on MQM annotations (erroneous
words, error type and error majority). In a second step, they ask a model to
calculate a score from these. They note the instabilities of ChatGPT, but find
that their approach is strong on a system-level and generates sentence-level
scores that are better than BERTScore. Xu et al. (2023) fine-tune
InstructScore, a LLaMA model (Touvron et al. 2023) that generates a text
containing error majority, error spans, error types (MQM) and an explanation
for every error in an input sentence. The generated texts are similar to Lu et
al. (2023) but add more details on each error. Similarly, they construct a
score from the count and severity of errors. They obtain synthetic training
data from GPT4 (OpenAI 2023a), analyze failure modes of InstructScore during
training and later optimize the metric on real machine translation outputs on
data sets with annotated ground truth scores. Their model shows state-of-the-
art results for reference-free sentence-level evaluations. Further, the
detailed error annotations can be used as fine-grained explanations.
Differently to the previous approaches, Bao et al. (2023) fine-tune a seq2seq
model to predict addition and omission errors on a word-level. In the example
presented in Figure 5, a tag “sense” flags the wrong word sense of “became”.
Notably, all fine-grained error approaches we describe are very recent, which
is potentially caused by the adaptation of MQM in the WMT metrics shared tasks
and the recent successes of LLMs.

Goals: These fine-grained error approaches can be used for increased
accessibility and semi-supervised labeling, as the methods model plausible
explanations (some of them by testing the alignment of scores aggregated from
explanations with human MQM scores). The usage for metric diagnosis is
limited, as only samples are explained, and explanations have not been tested
for faithfulness. The current approaches cannot be used to detect social
biases. Audiences: Flagged translation errors are simple to understand, such
that these techniques could be used by non-expert and expert users. Metric
developers might use them to create new data sets to train new metrics.
Limitations: The current approaches (besides Xu et al. 2023) do not have the
goal to provide explanations that make the sentence-level scores more
descriptive. Instead, they only search to build sentence-level metrics from
LLM outputs. As such, there might be open opportunities in leveraging the
explanations provided by these models to explain to a human explainee. Another
limitation are missing evaluations of faithfulness.

Perturbation robustness (Row 4 in Figure 4) The methods described up to now
addressed decision-understanding techniques Gehrmann et al. (2020). This
paragraph and the next describe model-understanding techniques (we describe
these terms in §2.2). Many recent works evaluate the robustness of MT metrics
(Sai et al. 2021; Karpinska et al. 2022; He et al. 2022; Sun et al. 2022; Chen
and Eger 2023; Vu et al. 2022). Robustness evaluation qualifies as model-
understanding, as it usually infers properties that describe a model (see
§2.2). All works in this category are structured into “black box” in Figure 4.

Sai et al. (2021) propose perturbation checklists, following Ribeiro et al.
(2020), that evaluate how susceptible metrics are towards predefined types of
perturbations as evaluation criterion. This allows developers to check whether
all invariances that are required for a specific task are fulfilled. In
particular, they compare the change in the metric’s score with the change in
score a human would assign after the perturbation. These templates encompass
dropping or adding of context as well as negations. They show that
perturbation checklists allow to pick metrics that are strong with respect to
specific properties. For example, their experiments indicate MoverScore (Zhao
et al. 2019) would capture fluency better than BERTScore (Zhang et al. 2020a)
due to its ability to cope with the jumbling of words. The checklists allow
for more fine-grained assessment of automatic evaluation metrics, which
exposes their limitations and supports the design of better metrics. Chen and
Eger (2023) create a similar, but fully automatic variant of this evaluation.
They design several perturbation types (framed as adversarial attacks) such as
addition, omission and negation. Then they measure whether a tested metric
prefers a meaning preserving, but lexically dissimilar paraphrase of a
source/reference sentence over a minimally attacked but lexically similar
version of the source/reference sentence. This means they check whether the
metric score of the attacked example is successfully lower than the metric
score of a paraphrase. One of their findings is that standard metrics often
have problems in detecting wrong names or numbers in translations. Karpinska
et al. (2022) create DEMETR, a data set of machine translations that were
perturbed using several perturbation types. They propose to test a metric’s
preference between these perturbations and the original translation. Their
task is easier for a metric to compute than Chen and Eger (2023), as the
preference is checked directly between attacked and non-attacked version,
while Chen and Eger (2023) use a dissimilar paraphrase instead of the non-
attacked version. Also, they define more perturbation types than Chen and Eger
(2023), but require manual annotation for some of them. He et al. (2022) also
compare the original translation with certain perturbation types. They differ
from the other two papers in terms of domains they are applying their method
to and in the perturbation types that they propose. Vu et al. (2022) use
character-level adversarial attacks to evaluate the robustness of several
BERTScore variants.

The WMT21 (Freitag et al. 2021b) and WMT22 (Freitag et al. 2022) metrics
shared tasks introduce a similar setting for robustness tests. In WMT21, the
organizers provided the participants with a challenge set composed of
translations that were perturbed in a specific way, to test whether metrics
reduce their score as expected when presented with respective phenomena. In
WMT22, participants of a challenge set subtask (Alves et al. 2022; Avramidis
and Macketanz 2022; Chen et al. 2022; Amrhein et al. 2022) designed data sets
posing challenges in a similar manner. In the example presented in Figure 5,
we show tests similar to those performed by techniques of this category. The
metric score should be decreased if numbers or word order used in the
hypothesis change.

The work by Guerreiro et al. (2023) is also related to these approaches. One
of their contributions is an expert annotated data set of machine translation
hallucinations. They use this data set to test how well selected metrics can
identify each hallucination type. Further, they give the recommendation to
simply use the sequence log-probability for hallucination detection.

While the previous techniques describe the susceptibility of metrics towards
various perturbation types, Sun et al. (2022) describe methods to analyze the
metrics’ social biases. They also base their evaluation on perturbed input
sentences; however, they evaluate whether a metric grades two versions of a
sentence that contain opposite social stereotypes the same with respect to a
neutral reference sentence (that does not contain stereotypes). They find that
generation-based metrics show the least biases.

Goals: The presented perturbation robustness approaches give detailed insights
into metric weaknesses. Therefore, they are most suited to be used for metric
diagnosis and improvement. Audiences: Metric developers can use these methods
for diagnosis. They can also be used by metric users to check if a metric fits
their use case. Regulatory entities might test if metrics fulfill their
guidelines, e.g. exhibit no social biases. Lastly, affected users might gather
evidence when treated unfairly. Limitations: A drawback of the perturbation-
based approaches is that they require predefined error types. Therefore,
errors lacking a definition in the evaluation scheme that would fool the
machine translation metrics could be missed. These approaches are faithful to
some degree, namely in the domains they have been evaluated in. For small
perturbation sets it could be questionable if the model generally behaves as
predicted. Therefore we would suggest evaluating each property on a large,
general data sample. Still, it could be advisable to employ faithfulness
tests, if possible, to achieve guarantees.

Linguistic properties (Row 5 in Figure 4) Here, we summarize model-
understanding techniques that directly summarize various linguistic properties
a metric fulfills. So far, the only method that falls into this category for
MT is Kaster et al. (2021) (see linguistic properties/black box in Figure 4).
They propose a model-understanding technique that decomposes the score of
sentence-level BERT-based metrics into linguistic factors. In particular, they
explore the degree to which metrics consider the properties syntax, semantics,
morphology and lexical overlap. To do so, they measure these properties in a
given data set and learn regressors that explain how much each distinct
property contributes to the original metric’s score. In their experiments,
they show that each metric captures semantic similarity and lexical overlap to
some degree. Syntactic and morphological similarity are either captured to a
smaller extent or are even negatively correlated with the metric score.
Especially, MoverScore (Zhao et al. 2019) and BERTScore (Zhang et al. 2020a)
have a comparatively high coefficient for the lexical score.

As shown in Figure 5, these techniques might give explanations like “the
metric puts a high focus on lexical properties (green bar) and a low focus on
semantics (red bar)“.

Note that the explanation of linguistic properties is close to the decision-
understanding of quality aspects. If these are collected over many samples and
a regression is performed, similar explanations might be achieved. A related
approach used for another NLP task is by Opitz and Frank (2022), who retrain
sentence embeddings to decompose into sub-components that match the aspects of
abstract meaning representation, a format to represent the meaning of
sentences. This allows them to attribute parts of the score to different
aspects of a sentence. They evaluate their approach on the model-understanding
level. When this would be applied to MT, it would also count towards this
explanation type.

We also list Kaster et al. (2021); Opitz and Frank (2022) with quality aspect
explanations, as the metrics that measure the linguistic factors can also be
computed on a sentence level.

Goals: The discussed linguistic properties approaches evaluate general metric
properties and can be used for metric debugging. As they generate
sentence/metric-level property scores, the techniques are not usable for word-
level semi-automatic labeling. When used on a sentence-level, the techniques
can aid the goal of making metrics more accessible. While not employed here,
the techniques might be usable to detect social biases by introducing a set of
bias detection scores from which to learn the regressors. Audiences: The
linguistic properties can be easily explained to non-experts, however metric
selection should not solely be performed based on linguistic properties and is
advised to be handled by domain experts. The methods are especially useful to
debug metrics, i.e. for metric developers. Limitations: The approach by Kaster
et al. (2021) could not explain reference-free metrics well, so plausibly
requires alternate explanatory variables. The search for regressors may be
inspired by quality aspect approaches (described earlier this section) where
not a global metric score is reported but several scores (such as coherence,
fluency, etc.). These could then compose a global MT score. Further, some of
the “property metrics” could be considered black box variables themselves and
future work might replace them by more transparent factors. One might also
explore the collinearity of the different regressors. For Kaster et al.
(2021), faithfulness is likely given to some degree, around the samples that
was trained on. Still, it could be advisable to employ faithfulness tests, if
possible, to achieve guarantees.

##  5 Future Work

Counterfactuals“Die Dinge wurden kompliziert”$\shortrightarrow$Score:1“Die
Dinge erhalten kompliziert”$\shortrightarrow$Score:0.5Natural Language
ExplanationsIn German, the verb ”bekommen” means ”to get” or”to receive,” but
in this context, it doesn’t convey theintended meaning effectively. Instead,
we can use theverb ”werden” which means ”to become” to better capture the
sense of change or transformation.Decision Rules“became”$\in$ Source &
“werden” $\in$ Hypothesis$\shortrightarrow$Score$>0.5$Grouped/Hierarchical
Feature ImportanceImportance((“became”,“bekommen”)):
0.1Importance((“Things”,“Die Dinge”)): 0.6Neuron ExplanationsNeuron 1423:
bekommenNeuron 5234: werdenExemplary FutureExplanation TypesExample
Data:Ground Truth: “Things became complicated”Hypothesis: “Die Dinge bekamen
kompliziert”Score: $0.8$

Figure 6: Exemplary future explanation types for MT metrics. For each type we
provide a hypothetical example. For this we assume the exemplary translation
that is shown in the top-left box. Here, the polysemic word ”became” is
translated with the wrong word sense ”to receive” instead of ”to get/to
develop into”. The correct translation is ”wurden”.

In this section, we describe future research directions for explainable MT
metrics. Specifically, we (1) consider directions for explanation types that
have been used in the papers described in our taxonomy, (2) describe potential
explanation types from other domains, (3) point out needs for future
evaluations and concepts and (4) urge for an explanation of domain boundaries.

Exploring present explanation types Based on our taxonomy (see Figure 4), we
identify unexplored areas of explainable MT evaluation (empty table cells). We
find that only feature importance techniques are thoroughly explored on all
levels of model access. The other methods mostly explain in black box settings
or are based on metrics that are explainable by design. We suppose that
gradients, attention weights and embeddings have so far mostly been applied in
the field of feature importance, as there is often already a 1-1 relationship
their values have towards input features. Also, due to the two recent shared
tasks, feature importance has likely received the most attention.

Future work could consider enhancing quality aspect explanations — Row 1 in
Figure 4 — by incorporating more model access. For example, a separate fluency
score might be realizable by employing measurements on target side embeddings
in unsupervised matching metrics. Current model-understanding techniques for
MT metrics — Row 4-5 in Figure 4 — are mostly applied in a black box manner.
For these, using more model access might also provide further insights. For
example, analyses of the contextualized embedding space and how it affects a
metric score could explain the metric on a more general level. Many works
consider the probing of transformer-based architectures (e.g. Tenney et al.
2019; Liu et al. 2019; Sajjad et al. 2022b) and it would be interesting to see
if their results can inform the metric development.

Also, current perturbation robustness techniques often use manually defined
perturbation strategies. It would be interesting to use adversarial attacks
instead that automatically infer these strategies.

Exploring other explanation types Besides the works in our taxonomy, other
fields of NLP and explainable AI in general have proposed further explanation
types, e.g. counterfactuals, natural language explanations and rule extraction
(e.g. Madsen et al. 2022). We illustrate these options with an example in
Figure 6. We already introduced counterfactuals in §2.2, stating their role in
evaluations of robustness (as adversarial attacks) and fairness. It remains to
explore if single sentence explanations also carry explanatory value. In the
examples for counterfactuals in Figure 6, the original word “bekommen” is
switched with the words “wurden” (to get) resp. “erhalten” (to receive) in two
counterfactuals. Based on the scores achieved by these counterfactuals, a user
could infer metric weaknesses. One technique to generate counterfactuals is
for example described by Wu et al. (2021).

Decision rule explanations describe rules which a model follows when computing
its output Ribeiro et al. (2018). Often, these techniques were designed to
explain classification models and are based on perturbations that change the
originally predicted class Ribeiro et al. (2018). For metrics, the scores
could be discretized beforehand. An example from other NLP domains are Anchors
Ribeiro et al. (2018). In our example (see Figure 6), the rule describes that
if “became” is present in the source and “wurden” is present in the
translation, the metric will assign a score higher than 0.5. Obviously in
other contexts, such a rule would fail.

Another type of explanations are hierarchical feature importance explanations,
which will assign importance scores to groups of input tokens in single Chen
et al. (2020) or paired input sentences Chen et al. (2021). In the given
hypothetical example (see Figure 6), the groups of became/bekommen and
Things/Die Dinge were formed and get assigned separate importance scores. A
related approach was tested in the SemEval-2015 and -2016 shared tasks on
semantic similarity, where the task was to assign labels that capture the
similarity between token groups from two sentences Agirre et al. (2015, 2016).

Natural language explanations are explanations in free text form (e.g.
Wiegreffe and Marasovic 2021). This is an explainability type where the
generative abilities of recent large language models (LLM), like ChatGPT and
GPT4, can be leveraged. The works by Fu et al. (2023); Lu et al. (2023); Kocmi
and Federmann (2023) already explore the use of LLMs as metrics and extract
the metric scores from LLM outputs (or generation probabilities) that contain
(or could easily be extended to provide) natural language descriptions of why
a certain score is assigned. In contrast, Xu et al. (2023) explicitly
highlight the explainability aspect of their model. In our taxonomy, we
grouped them under fine-grained error explanations, as this is their main
goal. However, they also produce natural language explanations for each
error.151515We again note that these techniques will not necessarily describe
a metric’s internal workings, but might be usable due to providing plausible
explanations, see §2.2. In these works, the explanations are rather used as a
byproduct, while an evaluation of the explanation quality and further use
cases remains for future work.

The example in Figure 6 was generated with the help of ChatGPT, which shows
that such an approach might yield plausible explanations. An example from
other NLP domains is Rajani et al. (2019), who generate explanations for
commonsense reasoning. In NLG in general, many other approaches for LLM-based
metrics have been constructed since the release of ChatGPT. These could
potentially be extended to MT metrics and explainability (e.g. Chiang and yi
Lee 2023; Wang et al. 2023; Ji et al. 2023a; Chen et al. 2023; Liu et al.
2023).

Recent works, describe prompting strategies like chain-of-thought (Wei et al.
2022) and tree-of-thought (Yao et al. 2023) where language models show
performance improvements if they are giving explanations of their output. It
could be an interesting research direction, to see if the explainability
techniques summarized in this paper can improve metric performance in this
way, or if these techniques can provide reasonable explanations (that are at
least plausible).

Neuron Explanations are another potential use case of LLMs to separately
explain each Neuron in the underlying networks. Bills et al. (2023) recently
showed that GPT4 can (to some extent) summarize highlighted neuron activation
in textual input of GPT2 to the same extent humans can. This allows to
successfully simulate the behavior and effects of some neurons. For MT
metrics, this would allow to determine the role certain neurons play to
determine scores when specific features like “became is used” are present in
the metric input. At the moment, these methods however still have some
limitations Bills et al. (2023).

Interactive Explanations Due to their context size, instruction fine-tuning
and emergent abilities of recent LLMs allow to interactively ask questions and
adapt based on the answers (e.g. OpenAI 2023b). Jacovi et al. (2023) state
that interactivity is a key point of successfully communicating the explained
subject to an explainee. LLM-based explanations (like natural language/neuron
explanations …) might achieve this. For example, a user could ask for more
detailed information for a mistranslation that was noted in a first
explanation.

New approaches could furth consider displaying multiple explanation types
together, like Xu et al. (2023) who present fine-grained error annotations
with natural language explanations.

Future evaluation and concepts Current work scarcely performs human evaluation
of explanations. This is partly because the focus is often not on the
explanations but other aspects, like the sentence-level scores. One form of
human evaluation (for plausibility) is often to use pre-computed human
explanations and check the correlation to them (e.g. Zerva et al. 2022).
Current works on MT metrics do, however, not evaluate whether the explanations
are actually helpful to explain the concepts they describe to their audience.
This means, whether the explanations manage to help the explainees to adapt
their own theory of mind to be a coherent abstraction of the real problem
Jacovi et al. (2023). Hence, we encourage future work to address this, e.g. by
considering simulatability Hase and Bansal (2020). Regarding plausibility, it
could also be interesting to evaluate whether plausible explanations of
incorrect metric scores can actually deceive non-experts or experts (with the
current state-of-the-art), as warned by Jacovi and Goldberg (2021); Jin et al.
(2023).

As noted throughout the paper, the degree of faithfulness an explanation has
to the metric’s internal decision process is an important property (Jacovi and
Goldberg 2020), especially for metric debugging. Current works on decision-
understanding of MT metrics, however, mostly dismiss faithfulness. Future work
might consider the evaluation of metric faithfulness, to enable these use-
cases. We note that this evaluation will be complex, due to faithfulness being
multi-faceted (Jacovi and Goldberg 2020). For example, lower-granularity
scores can faithfully explain sentence-level scores that are aggregated from
them, however this does not explain how the actual black box component
produced the lower-granularity scores. Further, there are pitfalls that exist
in faithfulness evaluation (Ju et al. 2022), warning against the usage of
common methods like area over the perturbation curve scores (AOPC) (Samek et
al. 2017). Therefore, future work should first consider what constitutes
faithful explanations in terms of metrics and design evaluation approaches.

Further, we note that current works mostly consider explaining sentence-level
scores. Future work, might also consider other metric granularity types more.

Exploring domain boundaries Future work might also consider under-explored use
cases of explainability for MT metrics. Currently, only one work considers the
social biases of MT metrics Sun et al. (2022) and none considers the usage of
explainability for semi-automatic labeling of training data. Besides examining
just the metrics, future work could also inspect the relation of explainable
MT metrics, especially plausible-only explanations, to explainable MT (e.g.
Stahlberg et al. 2018), as explanations of both will probably be similar.
Lastly, future work could explore providing similar taxonomies as ours that
are comprising _the whole field of explainable natural language generation
metrics_.

##  6 Related Work

A large number of conceptual works and surveys have been conducted in the area
of explainability (e.g. Lipton 2016; Biran and Cotton 2017; Adadi and Berrada
2018; Doshi-Velez and Kim 2017; Došilović et al. 2018; Guidotti et al. 2018;
Arya et al. 2019; Carvalho et al. 2019; Gilpin et al. 2019; Miller 2019;
Barredo Arrieta et al. 2020; Bodria et al. 2023; Linardatos et al. 2021).

There are few surveys that touch upon the need for explainable NLG (here MT),
thereby motivating our work. In their survey of NLG metrics, Celikyilmaz et
al. (2020) see the need for explainable evaluation metrics to spot system
quality issues and to achieve higher trust in the evaluation of NLG systems.
They consider that such quality issues might be unintended biases and factual
inconsistencies. Sai et al. (2022) instead propose that explainable NLG
metrics should focus on providing more information than just a single score
(such as fluency or adequacy). Gehrmann et al. (2023) also notice the need for
interpreting NLG metrics and give a brief overview of selected related
approaches. The main contribution of their work is an overview of common NLG
metric shortcomings and best practices to prevent them. Our focus on concepts
for explainable MT metrics is a different one and our taxonomy of approaches
is much more extensive.

General surveys on explainable NLP are related to our work, as they highlight
how explainability techniques have been used in NLP and might further guide
the development of future approaches for explainable MT metrics (e.g.
Vijayakumar 2023; Sajjad et al. 2022a; Gurrapu et al. 2023; Lertvittayakumjorn
and Toni 2021; Saha et al. 2022; Sun et al. 2021; Zini and Awad 2022; Qian et
al. 2021; Madsen et al. 2022). Besides these works, there are also surveys of
robustness (e.g. Goyal et al. 2023) and fairness (e.g. Blodgett et al. 2020),
which are related, as discussed in §2.2.

The taxonomy graphic in Figure 4 was adapted from the survey of post-hoc
explainability methods in NLP by Madsen et al. (2022). In specific, we follow
their approach to display model access on the x-axis and explanation types on
the y-axis. Further, we have customized their TIKZ code. We specifically focus
on the explainability of MT metrics; therefore, our taxonomy is completely
different content-wise. Our definitions of explainabilty, its goals and
audiences are in some parts adapted from the survey of Barredo Arrieta et al.
(2020) (see §2.2). Our work is different from previous surveys, as we set our
focus on the explainability of MT metrics.

##  7 Conclusion

In this work, we discuss audiences, goals and properties for _explainable
machine translation metrics_ , a nascent field that may help further overcome
the dominance of classical low-quality evaluation metrics. We also survey and
categorize recent approaches on explainable MT metrics into a taxonomy,
highlighting their results and limitations. Currently, two dominant approaches
to explainability for MT metrics are (1) feature importance explanations that
highlight erroneous words in source and hypothesis to explain sentence-level
scores and (2) perturbation robustness approaches that check a metric’s
robustness to manually devised types of input perturbations. We also identify
a current trend towards (3) fine-grained error explanations likely caused by
the recent improvements of LLMs like ChatGPT and by the adaptation of MQM
annotations in the WMT metrics shared tasks. A major weakness of the current
realization of (1) is that error highlights do not consider the correspondence
between words in source and target. Further, a weakness of (2) is that
perturbation types have to be manually defined, an issue that might be tackled
by using more general approaches of adversarial attacks in the future (we
discuss this issue in more detail in an earlier version of this paper;  Leiter
et al. 2022a). Generally, most current evaluations of explainability
approaches for MT metrics do not consider the faithfulness of explanations,
limiting their use in metric debugging and high-risk scenarios. We also
present a vision of next-generation approaches to explainable evaluation
metrics, which should help fix the problems of the above paradigms and provide
guidance to explore unexplored areas. Here, we also urge future work to
consider the desiderata and implications of faithfulness for explainable MT
metrics.

Our broader vision is that explainability is now a ‘desirable but optional’
feature, but we argue that in the future it will become essential, even
compulsory, especially for evaluation metrics as a highly sensitive task
assessing the quality (and veracity) of translated information content.
Explainability builds transparency and trust for users, eases bug-fixing and
shortens improvement cycles for metric designers and will be required by
law/regulations for AI systems to be applied to large-scale, high-stake
domains. In this context, we hope our work will catalyze efforts on the topic
of explainable evaluation metrics for machine translation.

#### Acknowledgements

Since November 2022 Christoph Leiter is financed by the BMBF project
“Metrics4NLG”. Piyawat Lertvittayakumjorn had been financially supported by
Anandamahidol Foundation, Thailand, from 2015-2021. He mainly contributed to
this work only until September 2022 while affiliated with Imperial College
London (before joining Google as a research scientist after that). Marina
Fomicheva mainly contributed to this work until April 2022. Wei Zhao is
supported by the Klaus Tschira Foundation, Heidelberg, Germany. Yang Gao
mainly contributed to this work before he joined Google Research in December
2021. Steffen Eger is financed by DFG Heisenberg grant EG 375/5–1 and by the
BMBF propject “Metrics4NLG”.

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