Socially Aware Bias Measurements for Hindi Language Representations

Abstract

Language representations are an efficient tool used across NLP, but they are strife with encoded societal biases. These biases are studied extensively, but with a primary focus on English language representations and biases common in the context of Western society. In this work, we investigate the biases present in Hindi language representations such as caste and religion associated biases. We demonstrate how biases are unique to specific language representations based on the history and culture of the region they are widely spoken in, and also how the same societal bias (such as binary gender associated biases) when investigated across languages is encoded by different words and text spans. With this work, we emphasize on the necessity of social-awareness along with linguistic and grammatical artefacts when modeling language representations, in order to understand the biases encoded.

Mitigating Gender Stereotypes in Hindi and Marathi

“…TGBI metric is used to detect and evaluate gender bias in Machine Translation systems. Malik et al (2021) measure Hindi specific societal biases like religion bias and caste bias along with gender bias.…”