Measuring and Mitigating Name Biases in Neural Machine Translation (2022)

Abstract: Neural Machine Translation (NMT) systems exhibit problematic biases, such as stereotypical gender bias in the translation of occupation terms into languages with grammatical gender. In this paper we describe a new source of bias prevalent in NMT systems, relating to translations of sentences containing person names. To correctly translate such sentences, a NMT system needs to determine the gender of the name. We show that leading systems are particularly poor at this task, especially for female given names. This bias is deeper than given name gender: we show that the translation of terms with ambiguous sentiment can also be affected by person names, and the same holds true for proper nouns denoting race. To mitigate these biases we propose a simple but effective data augmentation method based on randomly switching entities during translation, which effectively eliminates the problem without any effect on translation quality.

## introduction natural language processing systems are seeing widespread adoption, prompting careful study into cultural biases they exhibit, and methods for bias mitigation. gender bias is common in automated systems @xcite @xcite , with a leading cause being training corpora that include far more sentences referring to men than to women. a neural machine translation (nmt) system naïvely trained on such data is more likely to translate text that should be feminine into masculine when translating into a language with grammatical gender. previously, researchers @xcite @xcite have demonstrated that nmt systems can still be biased even when there are explicit gender pronouns in the input sentences. nmt systems are not only biased for gender, and gender bias is not limited to gender pronouns. other biases include racial biases, professional biases, and individual biases, among others. in this paper, we focus on two kinds of biases of person name translations by nmt systems: gender biases and sentiment biases. as an important category of named entity, person names are particularly sensitive to translation errors since they refer to realworld individuals, and systematic biases may cause serious distress to users, and reputational damage, libel or other legal consequences for vendors. gender bias in the translation of person names is a natural extension of gender biases in previous work. for instance, @xcite @xcite ) considered whether translation systems can translate keywords such as occupation terms into the correct form when there is explicit gender information in the text. this paper can be seen as replacing this explicit gender information (pronouns) with implicit gender information (person names), to test whether an nmt system can correctly determine the gender of a name. our results indicate that nmt systems often mistakes female names for males, but the reverse is rarely seen; a situation that may cause widespread offence. biases pertaining to sentiment of sentences containing person names have been studied in sentiment analysis @xcite , where model predictions of sentiment are sensitive to changing the person name. we present a method for detecting sentiment bias in translation based on the translation of sentiment ambiguous words, where the system must choose between a commendatory and derogatory translation (e.g., proud can mean either satisfied or arrogant about one's achievements). when the correct translation is not clear from the context, nmt systems use the person name to decide. when this occurs consistently towards a specific sentiment, this can result in insidious bias against (or towards) individuals (or as we also show, racial groups.) to mitigate the above biases against person names in translation, we propose a dataaugmentation method 'switch-entity' (se), which works by altering training sentences containing named entities by randomly switching the entities for other entities of the same type (e.g., with matching gender). this simple strategy normalises the distribution of named entities, such that all names are observed sufficiently many times and in a diverse range of contexts. this ensures gender signals are learned correctly, and also stops the translation system from associating the name with idiosyncracies of the contexts in which is appears, thus mitigating sentiment bias. modifying the training data carries the risk of degrading sentence quality, and thus degrading accuracy. although replacing a named entity with another does change sentence meaning, it is unlikely to compromise grammaticality or render the sentence semantically incoherent. our results show that se beneficially mitigates gender bias when translating names into gendered languages, which we show leads to more accurate morphological inflection in sentences with female entities. at the same time, it does not sacrifice accuracy: the bleu score of the se-trained model is the same as for standard training. ## gender bias on names in languages with rich grammatical gender, the gender of people referenced in a sentence will often affect the morphology of the other words in the sentence. for example, "[per] is a royal designer" translates into german as either ## sentiment biases in nmt training corpora, names appear in different contexts, which can result in sentiment biases for specific names. for instance, a popular celebrity is likely to appear in many more positive sentiment contexts than a reviled mafioso, which may mean a nmt system mistakenly associate person names with translation sentiment. we set about measuring whether this manifests in nmt output using templated ambiguous contexts in english in which the ambiguity must be resolved when translating into the target language. to do so we use sentiment ambiguous words: a kind of homograph which has both commendatory and derogatory meanings. this is illustrated in ## mitigating biases bias in nmt models are mainly caused by the training data, which is typically unbalanced, e.g., females are much rarer than males in the training corpus, leading to gender bias. one simple way to balance out gender biases is to add a number of female data to balance the ratio of male to female sentences. however, obtaining new data can be difficult, especially for low-resource languages. here, we propose a data augmentation method that does not require additional data, switchentity. by switching names in the training corpus, the model can train with more correct translation patterns about female names, so that the model can correctly identify the gender of the name, and achieve the effect of reducing biases. this method can be applied not only to person entities, but also to other classes of named entities. ## related work gender bias is a central concern in machine translation research. @xcite introduced the winomt challenge data set from the study of coreference gender bias @xcite to test the gender bias of machine translation systems. researchers tried many different methods to mitigate gender bias. saunders and byrne (2020) and costa-jussà and de jorge (2020) both used transfer learning to reduce gender bias by fine-tuning models with a small genderbalanced data set. stafanovics et al. (2020) annotated source language sentence with grammatical gender information from target language to reduce the stereotype of gender for translation systems. escudé @xcite used word embedding techniques, debiased the word embedding and then used these embeddings in training translationn models from scratch. all of this work was focused on sentences with gender pronouns, studying whether translation systems can correctly determine the grammatical gender of the words associated with gender pronouns. the gender bias we proposed in this paper is focused on names with implicit gender information. other social biases and stereotypes have also been investigated. @xcite evaluated gender and race biases on two hundred sentiment analysis systems, similar to our work, they also tested the influence of names on biases. @xcite examined racial biases on a hate speech task, finding that tweets written in african-american english are more likely to be marked as offensive than tweets written in standard american english. @xcite used pointwise mutual information to evaluate over the snli natural language inference data set, and uncover a wider range of biases, including gender, age, race, and nationality. @xcite is the closest to our work, they evaluated the sentiment bias in a language generation model on given names, finding evidence of bias whereby generated sentences related to specific given names being more negative than others. our mitigation method switchentity is based on data augmentation. similar methods of entity switching have been proposed for named entity recognition (ner), either for data augmentation in training to increase model coverage over named entities @xcite ; or during testing as a diagnostic tool to model generalization @xcite . @xcite proposed more general methods of random lexical substitutions for nmt, which designed to improve translation performance. @xcite use data augmentation for name entity translation by replacing source words with their corresponding target translation. ## conclusion in this paper, we revealed two biases in the nmt systems, gender biases and sentiment biases against names. our results show that the existing research models and commercial translation systems have serious biases, which not only affects translation quality, but also have ethical implications on fairness and bias. in order to mitigate biases, we proposed switchentity, a simple training strategy which can reduce name biases without the need for any additional data.