Are Multilingual Sentiment Models Equally Right for the Right Reasons? (2022)

Abstract: Multilingual NLP models provide potential solutions to the digital language divide, i.e., cross-language performance disparities. Early analyses of such models have indicated good performance across training languages and good generalization to unseen, related languages. This work examines whether, between related languages, multilingual models are equally right for the right reasons, i.e., if interpretability methods reveal that the models put emphasis on the same words as humans. To this end, we provide a new trilingual, parallel corpus of rationale annotations for English, Danish, and Italian sentiment analysis models and use it to benchmark models and interpretability methods. We propose rank-biased overlap as a better metric for comparing input token attributions to human rationale annotations. Our results show: (i) models generally perform well on the languages they are trained on, and align best with human rationales in these languages; (ii) performance is higher on English, even when not a source language, but this performance is not accompanied by higher alignment with human rationales, which suggests that language models favor English, but do not facilitate successful transfer of rationales.

## introduction nlp models are sometimes right for the wrong reasons, e.g., when sentiment analysis models correctly predict a movie review to be positive because it contains the word @xcite . human rationale annotations can be used to evaluate the extent to which models are right for the right reasons, i.e., whether model rationales align with human rationales. datasets with rationale annotations exist for sentiment analysis @xcite , fact-checking @xcite , natural language inference @xcite , and hate speech detection @xcite ), 1 1 several of these datasets can also be found in the eraser benchmark @xcite . but so far only for the english language. while multilingual language models often fail to generalize across distant languages @xcite @xcite , they do bridge between related languages and have become a standard solution to data sparsity @xcite , as well as a way to reduce the overall energy consumption of training language-specific language models @xcite . benchmark performance does not tell us whether multilingual models are more prone to spurious correlations in some languages rather than others, i.e., whether models are equally right for the right reasons or to different degrees, see this paper presents a trilingual parallel corpus of human rationale annotations in danish, italian, and english, for the task of sentiment analysis. to this end, we translate an existing sentiment analysis dataset into different languages following a similar procedure as @xcite , with human postcorrection. we then collect rationales from native speakers of these languages. we evaluate the quality of our human rationale annotations in two ways: using inter-annotator agreement metrics and using human forward prediction experiments @xcite . we then use the corpus to evaluate the extent to which multilingual language models are equally right for the right reasons across languages, and whether agreement with human rationales aligns with downstream performance. contributions our contributions are as follows: (a) we present a trilingual corpus of human rationales, based on post-corrected translations of the stanford sentiment treebank @xcite and annotated by native speakers. the corpus is made publicly available at https://github. com/rasmuskaer/blackbox2022 . (b) we propose better metrics for comparing ranked rationales than has previously been used, as well as a sequence-wise normalization of lime's token scores to make scores comparable across sequences. (c) we evaluate mbert @xcite and xlm-r @xcite , in conjunction with two interpretability methods, lime @xcite and shap (lundberg and lee, 2017), across three languages, quantifying the extent to which these models are equally right for the right reasons. ## multilingual rationale annotation our multilingual corpus of human rationales is based on post-corrected translations of the stanford sentiment treebank. we obtain danish and italian translations of a sample of validation data, correct the translations manually, and have native speakers annotate the original english sentences, as well as their post-corrected translations. we then validate the annotations by quantifying human inter-annotator agreement and by performing human forward prediction experiments @xcite @xcite @xcite . we describe each step in detail in this section. ## comparing ranked rationale lists to evaluate the agreement between human rationales and rationales identified by interpretability methods applied to automatic sentiment analyses, we need a similarity measure for comparing ranked rationale lists. common correlation tests are not sufficient, because the measure must be applicable to non-conjoint, uneven lists and should put a higher weight on higher-ranked words. the human annotator selects the most relevant words in a sentence until exhausted. the ranking is ordered, but may only contain a few words. on the other hand, the interpretability methods provide by design a rank for each word in a sentence. thus, the annotator's ranking is typically incomplete (not all items are ranked), while the automatically computed ranking is complete. that is, the two rankings are mutually non-conjoint. furthermore, we need to deal with indefiniteness @xcite in the sense that the annotator may truncate the complete list at an arbitrary depth. the mea-sure we propose for evaluating rationale rankings is the extrapolated version of the rank-biased overlap @xcite , rbo ext , which is a generalization of average based overlap for indefinite rankings. it ranges from 0 (disjoint) to 1 (identical). the rbo ext measure satisfy the criteria needed for evaluating the agreement of list rationale rankings of both sentences and documents by being able to handle tied ranks, rankings of different lengths and top-weighted rankings. the degree of top-weightedness is determined by a parameter @xmath0, 1]. consider a person comparing two rankings by sequentially going through the lists starting with the highest rank. in each step, one additional rank is considered. that is, in the beginning only the highest ranked elements are compared, then additionally the top two elements are compared, and so on. at each step, the person stops the comparison with a probability 1p. roughly speaking, rbo ext measures the expected similarity computed by this randomized comparison. the parameter p induces a weighting of the ranks that decreases with decreasing rank (i.e., decreasing importance). following @xcite , we choose p such that 86% of the weight is concentrated on the first d ranks. they show that the concentration of weights on the first d ranks given p can be computed as ## experiments our experiments below rely on two pretrained multilingual language models, which we briefly introduce, three different experimental protocols, and two different interpretability methods. pretrained language models the experimental protocol is based on two pretrained multilingual transformer language models @xcite , namely mbert @xcite 3 and xlm-r @xcite foot_1 . we used the base, cased version from the hugging face transformers library foot_2 . following @xcite , we added a classification layer on top of the [cls] token. we fine-tuned these models for 3 epochs on a single tesla k80 gpu, with a training batch size of 16 and a learning rate of 3 • 10 -5 . the parameters were found using manual hyperparameter tuning based on the authors' recommendations of batchsizes {16, 32}, epochs {2, 3, 4}. the learning rate was fine-tuned over {2 • 10 -5 , 3 • 10 -5 , 5 • 10 -5 } with 3 trials each. ## analysis in this section, we present our analysis of our results and findings. first, we address whether models are equally right for the right reasons and how performance compares to agreement. next, we analyze the translations and the post-corrections. lastly, we examine whether token scores predict human rationales. are models equally right for the right reasons across languages? the idea of being right for the right reasons refers to learning from reliable signals in your data, which are causally related to the ground truth classification. while some models can be used to illuminate complex causal dynamics, others adapt clever hans strategies of relying on pervasive, yet spurious correlations in the training data. in this paper, we ask if multilingual language models such as mbert and xlm-r are equally prone to spurious correlations across languages? or could it be that these models adopt clever hans strategies for some languages, but not for others? our results show, very consistently, that mbert and xlm-r are less right for the right reasons for danish: when the training language is english or italian, or when multilingual training language is used, danish never aligns best with human rationales. for english and italian, it comes in worst in 18/20 cases, and in the multilingual setting, danish is least right for the right reasons in 6/10 cases. for english and italian, things are more or less on par. while english is slightly higher in rank agreement, then italian obtains a better span agreement, but the lowest span and rank agreement is generally seen with danish as the target language. we conclude that multilingual language models are not equally right for the right reasons across languages. how indicative is accuracy for agreement? it seems intuitive that a good model with high performance will also align better with human rationales, but theoretically, models may adopt radically different strategies, if multiple strategies are possible. even if we expect a positive correlation between performance and alignment, how strong is this correlation in practice? to answer this question, we compute the correlation between the accuracy of the language models and the agreement of span and rank. we use spearman's rank-order correlation test and pearson's correlation test, across both explanation methods and all datasets. both tests show that performance is only weakly (positively) correlated with alignment with human rationales; see lang. spearman's ρ pearson's ρ acc/auc 0.059 * * 0.092 * * acc/rbo 0.076 * * 0.153 * * humans may base their rationales on different parts than machine-based rationales. while humans consider and necessary for the snippet of deep and meaningful (see example in ## related work transformer-based multilingual models have been analyzed in many ways: researchers have, for example, looked at performance differences across languages @xcite , looked at their organization of language types @xcite , used similarity analysis to probe their representations @xcite , and investigated how learned self-attention in the transformer blocks affects different languages @xcite . human rationales have been used to supervise attention for various text classification tasks, such as sentiment analysis @xcite and machine translation @xcite . feature attribution methods such as lime and shap have also been applied to multilingual models: lime has been applied to mbert for analysis of hate speech models @xcite , and shap has been applied to mbert in biomedical nlp @xcite . lime has also been applied to xlm-r in the context of hate speech (socha, 2020), as well as in a biomedical context @xcite . shapley values have also been used to estimate the influence of source languages on the final predictions of models based on mbert @xcite . none of these applications have been evaluated, however. feature attributions have been applied to monolingual models, especially for english, more often than multilingual models. for english, we have a set of datasets with human rationales that we can use to evaluate feature attribution methods. these include beeradvocate @xcite ) and e-snli @xcite , as well as other datasets, several of which were collected in the eraser benchmark @xcite . the reason feature attribution methods have not been properly evaluated in a multilingual context, is simple: there was, until now, no gold standard with which to evaluate the rationales produced by multilingual models. ## conclusions we introduced a new trilingual, parallel corpus of human rank and span rationales in three related languages, english, danish and italian. we proposed rank-biased overlap as a better metric for rank evaluation when common correlation tests are not sufficient. we found that a sequence-wise normalization of lime's token scores is required to make scores comparable across sequences. evaluations on the corpus showed that generally, models perform well on the languages they are trained on, and align best with human rationales in these languages. models can be right for different reasons. the main results suggest that multilingual models are not equally right for the right reasons in the sense that interpretability methods indicate that the models not necessarily put emphasis on the same words as humans. we also observed that performance is high on english, even when it is not a source language, but that this superior performance is not accompanied by higher alignment with human rationales. in other words, this zero-shot advantage of english as a target language seems to come at the cost of being more prone to spurious correlations. with this work, we hope to inspire further progress on multilingual interpretation and collection of rationales in different languages.