Evaluating Gender Bias in Hindi-English Machine Translation

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.

Re-contextualizing Fairness in NLP: The Case of India

“…This is especially troubling for India, a pluralistic nation of 1.4 billion people, with fast-growing investments in NLP from the government 3 , and the private sector 4 . There is commendable recent work on NLP fairness in Indian languages like Hindi, Bengali, Telugu (Pujari et al, 2019;Malik et al, 2021;Gupta et al, 2021). But, for a nation with many religions, ethnicities, and cultures, recontextualizing NLP fairness needs to account for the various axes of social disparities in the Indian society, their proxies in language data, the disparate NLP capabilities in Indian languages, and the (lack of) resources for bias evaluation.…”