Evaluating Gender Bias in H indi- E nglish Machine Translation (2021)

Abstract: With language models being deployed increasingly in the real world, it is essential to address the issue of the fairness of their outputs. The word embedding representations of these language models often implicitly draw unwanted associations that form a social bias within the model. The nature of gendered languages like Hindi, poses an additional problem to the quantification and mitigation of bias, owing to the change in the form of the words in the sentence, based on the gender of the subject. Additionally, there is sparse work done in the realm of measuring and debiasing systems for Indic languages. In our work, we attempt to evaluate and quantify the gender bias within a Hindi-English machine translation system. We implement a modified version of the existing TGBI metric based on the grammatical considerations for Hindi. We also compare and contrast the resulting bias measurements across multiple metrics for pre-trained embeddings and the ones learned by our machine translation model.

## introduction there has been a recent increase in the studies on gender bias in natural language processing considering bias in word embeddings, bias amplification, and methods to evaluate bias @xcite , with some evaluation methods introduced primarily to measure gender bias in mt systems. in mt systems, bias can be identified as the cause of the translation of gender-neutral sentences into gendered ones. there has been little work done for bias in language models for hindi, and to the best of our knowledge, there has been no previous work that measures and analyses bias for mt of hindi. our approach uses two existing and broad frameworks for assessing bias in mt, including the word embedding fairness evaluation @xcite and the translation gender bias index @xcite on hindi-english mt systems. we modify some of the existing procedures within these metrics required for compatibility with hindi grammar. this paper contains the following contributions: all our codes and files are publicly available. foot_0 the prevalence of social bias within a language model is caused by it inadvertently drawing unwanted associations within the data. previous works that have addressed tackling bias include @xcite , which involved the use of multiple gender-definition pairs and principal component analysis to infer the direction of the bias. in order to mitigate the bias, each word vector had its projection on this subspace subtracted from it. however, this does not entirely debias the word vectors, as noted in @xcite . there have been various attempts to measure the bias in existing language models. huang et al. (2020) measure bias based on whether the sentiment of the generated text would alter if there were a change in entities such as the occupation, gender, etc. @xcite performed experiments on evaluating the bias in bert using the word embedding association test (weat) as a baseline for their own metric, which involved calculating the mean of the log probability bias score for each attribute. concerning the measurement of bias in existing mt systems, @xcite came up with a method to evaluate gender bias for 8 target languages automatically. their experiments aligned translated text with the source text and then mapped the english entity (source) to the corresponding target translation, from which the gender is extracted. most of the focus in mitigating bias has been in english, which is not a gendered language. languages like hindi and spanish contain grammatical gender, where the gender of the verbs, articles, adjectives must remain consistent with that of the gender of the noun. in @xcite a modified version of weat was used to measure the bias in spanish and french, based on whether the noun was inanimate or animate, with the latter containing words like 'doctor,' which have two variants for 'male' and 'female' each. @xcite worked on addressing the problem with such inanimate nouns as well and attempted to neutralize the grammatical gender signal of these words during training by lemmatizing the context words and changing the gender of these words. while there has been much work on quantifying and mitigating bias in many languages in nlp, the same cannot be said for hindi and other indic languages, possibly because they are low-resource. @xcite was the first work in this area; they use geometric debiasing, where a bias subspace is first defined and the word is decomposed into two components, of which the gendered component is reduced. finally, svms were used to classify the words and quantify the bias. ## results and discussion the bleu score of the opennmt model we used was 24.53, and the ribes score was 0.7357 across 2478 samples. ## conclusion and future work in this work, we have attempted to gauge the degree of gender bias in a hi-en mt system. we quantify gender bias (so far only for the gender binary) by using metrics that take data in the form of queries and employ slight modifications to tgbi to extend it to hindi. we believe it could pave the way to the comprehensive evaluation of bias across other indic and/or gendered languages. through this work, we are looking forward to developing a method to debias such systems and developing a metric to measure gender bias without treating it as an immutable binary concept.