Global Explainability of BERT -Based Evaluation Metrics by Disentangling along Linguistic Factors (2021)

Abstract: Evaluation metrics are a key ingredient for progress of text generation systems. In recent years, several BERT-based evaluation metrics have been proposed (including BERTScore, MoverScore, BLEURT, etc.) which correlate much better with human assessment of text generation quality than BLEU or ROUGE, invented two decades ago. However, little is known what these metrics, which are based on black-box language model representations, actually capture (it is typically assumed they model semantic similarity). In this work, we use a simple regression based global explainability technique to disentangle metric scores along linguistic factors, including semantics, syntax, morphology, and lexical overlap. We show that the different metrics capture all aspects to some degree, but that they are all substantially sensitive to lexical overlap, just like BLEU and ROUGE. This exposes limitations of these novelly proposed metrics, which we also highlight in an adversarial test scenario.

## introduction evaluation metrics are a key ingredient in assessing the quality of text generation systems, be it machine translation, summarization, or conversational ai models. traditional evaluation metrics in machine translation and summarization, bleu and rouge @xcite , have measured lexical n-gram overlap between system prediction and a human reference. while simple and easy to understand, early on, limitations of such lexical overlap metrics have been recognized @xcite , e.g., in that they can only measure surface level similarity, and they are especially inadequate when it comes to assessing current high-quality text generation systems @xcite @xcite . recently, a class of novel evaluation metrics based on bert and its variants has been explored that correlates much better with human assessments of translation quality. for example, bertscore @xcite , moverscore @xcite , bleurt @xcite , xmover-score @xcite , and comet @xcite all use large-scale pretrained language models, but differ in whether they compare hypotheses to references, to source texts, to both, on the one hand, and whether they use human scores for supervision or not, on the other. since these models all leverage large-scale language models which have pushed the state-of-the-art in many areas of nlp, their success comes with little surprise. to better understand these novel metrics based on black-box language representations is a prerequisite for identifying their limitations, e.g., to adversarial inputs. for example, if an evaluation metric is sensitive to lexical overlap, it can be fooled by using the same words but in different order. in this work, we fill the existing 'explainability gap' and introspect linguistic properties encoded in bert-based evaluation metrics. although there is already considerable work on introspecting and understanding bert (see @xcite for an overview), e.g., via probing @xcite , analyzes by @xcite ; @xcite ; @xcite indicate that probing results (based on supervision) are not always trustworthy. more importantly, the modern evaluation metrics sketched above rely on at least two factors: bert (or its variants) and different aggregation schemes, such as earth mover distance @xcite or greedy alignment @xcite , on top of bert. understanding bert alone is thus not sufficient for explaining bert-based evaluation metrics. here, we present a simple global explanation technique of bert-based evaluation metrics which disentangles them on prominent linguistic factors, viz., syntax, semantics, morphology and lexical overlap. we find that all metrics capture these linguistic aspects to certain (but differing) degrees, and they are particularly sensitive to lexical overlap, which makes them prone to similar adversarial fooling (cf. @xcite @xcite as bleu-based lexical overlap metrics. overall, our contributions are: our work concerns reference-based and referencefree metrics, on the one hand, and model introspection (or 'explainability'), on the other. ## our approach in our scenario, we consider different metrics m taking two arguments and assigning them a real-valued score m : (x, y) → s @xmath0, respectively (for so-called reference-free metrics) or alternatively x and y are reference and hypothesis text, respectively (for so-called reference-based metrics). the scores s that metrics assign to x and y can be considered the similarity between x and y or adequacy of y given x. in our experiments in section 4, we will focus on machine translation (mt) as use case; it is arguably the most popular and prominent text generation task. thus, x is either a (sentence-level) source text in one language and y the corresponding mt output, or x is the human reference for the original source text. to better understand evaluation metrics, we decompose their scores s m along multiple linguistic factors. an example is outlined in we follow a long line of research in the applied sciences, and use a linear model to explain a target variable (in our case, the metric score), also called response variable, in terms of multiple regressors, also called explanatory variables. that is, we estimate the linear regression m(x, y) = α • sem(x, y) + β • syn(x, y) here, sem(x, y), syn(x, y), morph(x, y) and lex(x, y) are scores which describe the semantic, syntactic, morphological, and lexical similarity between the two argument sentences. the real coefficients α, β, γ and δ are the regressors' weights, estimated from data. finally, is an error term. linear regression assumes a linear relationship between the target variable and the regressors. it may fail when the relationship is non-linear, but its simple model structure provides interpretability: a larger positive coefficient means the respective regressor has higher positive impact on the target variable (fixing all other variables), a coefficient close to zero means no linear relationship, and a larger negative coefficient means an inverse linear relationship between regressor and target variable. the coefficient of determination r 2 describes how well the regression model reflects the data. it is defined as r 2 = 1 -sse sst where sse denotes the sum of squared errors and sst denotes the sum of squared totals. they are defined as to ensure comparability of the different regressions, we normalize the scores of our regressors and the response variable with the z-normalization, i.e., subtracting the mean and dividing by the standard deviation, per variable. in the following, we define our regressors. semantic score (sem) the semantic scores are provided by the datasets and were annotated by humans who rated e.g. the translation quality. see section 4.2 for details. ## experiments we analyze different evaluation metrics by calculating their score for sentence pairs. we use both reference-based metrics, which operate in a monolingual space, and reference-free metrics, which operate in a cross-lingual space. ## results all metrics have comparatively large coefficients for lexical overlap, especially on wmt: the coefficient values range from 0.24 to 0.64 on wmt and from 0.14 to 0.67 on sts. especially lex dominates for moverscore and bertscore, indicating that these two metrics are most sensitive to lexical adversaries, potentially making them vulnerable to inputs such as 'man bites dog' vs. 'dog bites man'. reference-free metrics to explore why r 2 scores are now lower, we note that reference-free metrics based on bert might contain a form of cross-lingual bias (clb) in that they do not properly score mutual translations, as @xcite and many others have shown that the multilingual subspaces induced by bert are mis-aligned. we thus include a factor clb as a regressor to measure how significant this bias is in different metrics. note that the metrics use either different bert variants or other representations such as laser, which points to different sources of clb. therefore, we realize clb differently across metrics. for each metric regression, we use the same metric but take a parallel sentence, i.e., source text and google translation (as we assume that google translate has very high quality in general), as input arguments, and take the metric score as a proxy of the clb factor. if a metric does not contain cross-lingual bias, it should assign almost full scores to parallel sentences; this constant would then be meaningless in the regression. in limitations the r 2 scores for the wmt dataset are almost always lower than the corresponding sts scores. one may not forget that sts sentences are in a sense artificial sentences of the form 'a girl is playing a guitar' while wmt contains more realistic sentences as well as their (possibly faulty, non-grammatical) translations. the wmt datasets are furthermore inhomogeneous in that we used different years from 2015 to 2017, which has dif ## conclusions we disentangled bert-based evaluation metrics along four linguistic factors: semantics, syntax, morphology, and lexical overlap. the results indicate that (i) the different metrics capture these different aspects to different degrees but (ii) they all rely on semantics and lexical overlap. the first observation indicates that combining metrics may be helpful, which we confirmed: simple parameterfree averaging of hetereogenous metric scores can improve correlations with humans by up to more than 13% in our experiments. the second observation shows that these metrics may be prone to adversarial fooling, just like bleu and rouge, which we confirmed in an additional experiment in which we queried metric preferences over paraphrases with little lexical overlap and non-paraphrases with high lexical overlap. future metrics should especially take this last aspect into account, and improve their robustness to adversarial conditions. there is much scope for future research, e.g., in developing better global explanations for referencefree metrics (as we cannot yet well explain these metrics), better linguistic factors (e.g., a clearer conceptualization of morphological similarity of two sentences) and in developing local explainability techniques for evaluation metrics @xcite ). 2