Mitigating Gender Stereotypes in Hindi and Marathi

Neeraja Kirtane, Tanvi Anand


Abstract
As the use of natural language processing increases in our day-to-day life, the need to address gender bias inherent in these systems also amplifies. This is because the inherent bias interferes with the semantic structure of the output of these systems while performing tasks in natural language processing. While research is being done in English to quantify and mitigate bias, debiasing methods in Indic Languages are either relatively nascent or absent for some Indic languages altogether. Most Indic languages are gendered, i.e., each noun is assigned a gender according to each language’s rules of grammar. As a consequence, evaluation differs from what is done in English. This paper evaluates the gender stereotypes in Hindi and Marathi languages. The methodologies will differ from the ones in the English language because there are masculine and feminine counterparts in the case of some words. We create a dataset of neutral and gendered occupation words, emotion words and measure bias with the help of Embedding Coherence Test (ECT) and Relative Norm Distance (RND). We also attempt to mitigate this bias from the embeddings. Experiments show that our proposed debiasing techniques reduce gender bias in these languages.
Anthology ID:
2022.gebnlp-1.16
Volume:
Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP)
Month:
July
Year:
2022
Address:
Seattle, Washington
Venue:
GeBNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
145–150
Language:
URL:
https://aclanthology.org/2022.gebnlp-1.16
DOI:
10.18653/v1/2022.gebnlp-1.16
Bibkey:
Cite (ACL):
Neeraja Kirtane and Tanvi Anand. 2022. Mitigating Gender Stereotypes in Hindi and Marathi. In Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP), pages 145–150, Seattle, Washington. Association for Computational Linguistics.
Cite (Informal):
Mitigating Gender Stereotypes in Hindi and Marathi (Kirtane & Anand, GeBNLP 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.gebnlp-1.16.pdf
Dataset:
 2022.gebnlp-1.16.dataset.zip