Multilingual Holistic Bias: Extending Descriptors and Patterns to Unveil Demographic Biases in Languages at Scale (2023)

Abstract: We introduce a multilingual extension of the HolisticBias dataset, the largest English template-based taxonomy of textual people references: Multilingual HolisticBias. This extension consists of 20,459 sentences in 50 languages distributed across 13 demographic axes. Source sentences are built from combinations of 118 demographic descriptors and three patterns, excluding nonsensical combinations. Multilingual translations include alternatives for gendered languages that cover gendered translations when there is ambiguity in English. Our dataset is intended to uncover demographic imbalances and be the tool to quantify mitigations towards them. Our initial findings show that translation quality for EN-to-XX translations is an average of almost 8 spBLEU better when evaluating with the masculine human reference compared to feminine. In the opposite direction, XX-to-EN, we compare the robustness of the model when the source input only differs in gender (masculine or feminine) and masculine translations are an average of almost 4 spBLEU better than feminine. When embedding sentences to a joint multilingual sentence representations space, we find that for most languages masculine translations are significantly closer to the English neutral sentences when embedded.

## introduction demographic biases are relatively infrequent phenomena but present a very important problem. the development of datasets in this area has raised the interest in evaluating natural language processing (nlp) models beyond standard quality terms. this can be illustrated by the fact that machine translation (mt) models systematically translate neutral source sentences into masculine or feminine depending on the stereotypical usage of the word spa_latn soy ama de casa. </@xmath0. ref: i'm a homemaker figure 1 : pathological examples (in sentence piece) of multilingual holisticbias source and nllb translation, with spanish masculine/feminine references. input attributions @xcite of sentence piece in bold are shown in red gradients. (1) english is translated into spanish, and we observe masculine form, which could be overgeneralisation or a stereotype. (2) english is translated into spanish masculine for "amigos", which can be seen an overgeneralisation, but into feminine for "amas de casa", which is a stereotype. (3) is translated into feminine, which given the lack of translations into feminine, we assume is a stereotypical translation. (4) spanish masculine/feminine human translations used as source, nllb translations and english as reference. these examples illustrate the lack of gender robustness, supported by similar input attributions. while gender is one aspect of demographic biases, we can further explore abilities, nationalities, races or religion and observe other generalizations of the models that may perpetuate or amplify stereotypes and inequalities in society. quantifying and evaluating these biases is not straightforward because of the lack of datasets and evaluation metrics. proper evaluation will enable mitigation of these biases. related work holisticbias @xcite is an english dataset built from templated sentences that can elicit enough examples in various contexts to analyze and draw actionable conclusions: when measuring toxicity after translating holisticbias prompts @xcite ; when measuring the relative perplexity of different sentences as a function of gendered noun or descriptor @xcite ; when looking at skews of the usages of different descriptors in the training data, etc. other datasets consisting of slotting terms into templates were introduced by @xcite @xcite @xcite , to name a few. the advantage of templates is that terms can be swapped in and out to measure different forms of social biases, such as stereotypical associations. other strategies for creating bias datasets include careful handcrafting of grammars @xcite , collecting prompts from the beginnings of existing text sentences @xcite , and swapping demographic terms in existing text, either heuristically @xcite or using trained neural language models @xcite . most of these alternatives cover few languages or they are limited in the bias scope (e.g. only gender @xcite @xcite @xcite @xcite ). contributions our work approaches this problem by carefully translating a subset of the holisticbias dataset into 50 languages (see appendix a for a complete list), covering 13 demographic axes. as an extension of holisticbias, we will invite additions and amendments to the dataset, in order to contribute to its establishment as a standardized method for evaluating bias for highly multilingual nlp models. we use the proposed dataset to experiment on mt and sentence representation. results when translating from english show an average of almost 8 spbleu reduc-tion when evaluating on the feminine reference set compared to masculine. this showcases the preference towards masculine translations. among the 13 demographic axes of holisticbias, the quality of translation averaged across languages is highest for the nationality axis and lowest for the cultural axis. results when translating to english show that the masculine set has almost 4 spbleu improvement compared to the feminine set. when embedding sentences to a joint multilingual sentence representations space which is the core tool of multilingual data mining, we find that for most languages, there is a significant difference in the similarity between the masculine translations and the feminine one. masculine translations are significantly closer to the english sentence when embedded, even if this difference remains small and we do not yet know the effect on the mining algorithm. ## background: holisticbias holisticbias is composed of 26 templates, more than 600 descriptors (covering 13 demographic axes) and 30 nouns. overall, this dataset consists of over 472k english sentences used in the context of a two-person conversation. sentences are typically created from combining a sentence template (e.g., "i am a [noun phrase]."), a noun (e.g., parent), and a descriptor (e.g., disabled). the list of nearly 600 descriptors covers 13 demographic axes such as ability, race/ethnicity, or gender/sex. the noun can imply a certain gender (e.g. woman, man) or avoid gender references (e.g. child, kid). sentence templates allow for both singular and plural forms of the descriptor/noun phrase. experiments in mt with the nllb model using the full initial (english only) version of the holisticbias dataset, as reported in @xcite , show that the percentage of true added toxicity is also relatively low (from 0.004% in chinese to 1.07% in kinyarwanda) but that the number of examples in absolute value is much greater (20 in chinese, 4,951 in kinyarwanda) due to the fact that holisticbias is composed of roughly 230 times more sentences than the flores-200 dev and devtest sets put together. the templated nature of holisticbias also makes it possible to observe different translation behaviors for the same lexical items in different contexts. even if it is ideal for prompting english language models and mt from english to other languages, the main shortcomings of the holisticbias dataset are that we cannot evaluate how the quality varies for this particular domain; and we cannot study biases in a variety of languages, which affects multilingual nlp applications. holisticbias successfully combines sentence patterns, descriptors and nouns. however, when considering its extension to multiple languages, we did not consider a translation of patterns, descriptors and nouns because languages don't have the same, universal lexical, syntactic or morphological schemes, and not all languages allow for the use of templating/slotting like was done for english. instead, we did a full translation of selected sentences in holisticbias as described in the next section. ## multilingual holisticbias holisticbias is a large dataset and it is unfeasible to translate sentence-by-sentence to a large multilingual set of languages. in this section, we describe the data selection criteria and we report the statistics of the final multilingual holisticbias dataset. translation guidelines are reported in appendix b. ## machine translation evaluation in this section we use multilingual holisticbias to evaluate the quality of translations and compare performances across the gendered sets and we do a demographic analysis. ## multilingual sentence embeddings sentence representations are used, among others, to compute data mining of multilingual sentences and create training datasets for multilingual translation models (see @xcite ). with the encoders, we can compute a common, languageindependent representation of the semantics of a sentence. this is the case for laser @xcite and labse @xcite . ideally, the encoders should be able to encode the ambiguous english sentences so that they are equidistant from the gendered versions in the gendered languages. thus, we should expect "i'm a handicapped person" to be at the same distance in the embedding space as "je suis handicapé" (masculine french) and "je suis handicapée" (feminine french) as they would both be expressed the same in english. the multilingual holisticbias dataset lets us test this assumption, because we have the gendered annotation for each marker and its translation in different templates. ## conclusions we present a multilingual extension of the holisticbias dataset of approximately 20,500 new sentences. this multilingual holisticbias dataset includes the translations of 3 different patterns and 118 descriptors in 50 languages. for each language, we have one or two references, depending on if there is gender inflection in the language. each translated sentence includes the masculine/neutral translation and a second translation with the feminine alternative if it exists. our dataset is meant to be used to evaluate translation quality with demographic details and study demographic representations in data. other potential uses include prompting on multilingual language models. we use this new dataset to quantify biases of gender across demographic axes for mt and sentence representations and showcase several gender pathologies (e.g. overgeneralisation to masculine, gendered stereotypes, lack of gender robustness and wrong meaning). mt has higher performance for masculine sets than for feminine. for en-to-xx translations, performance increases over 8 sp-bleu. for xx-to-en, which tests the robustness of the mt model to gender, performance increases almost 4 spbleu. in terms of demographics, we see lower performance for those axis where there seems to be a higher masculine stereotype, e.g. socioeconomic status ("one-percenter"). multilingual embeddings show that they can be a source of bias, because for most languages, there is a significant (@xmath1.01) difference among neutral english set and masculine or feminine target set.