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.- Anthology ID:
- 2021.gebnlp-1.3
- Volume:
- Proceedings of the 3rd Workshop on Gender Bias in Natural Language Processing
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Venue:
- GeBNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 16–23
- Language:
- URL:
- https://aclanthology.org/2021.gebnlp-1.3
- DOI:
- 10.18653/v1/2021.gebnlp-1.3
- Cite (ACL):
- Krithika Ramesh, Gauri Gupta, and Sanjay Singh. 2021. Evaluating Gender Bias in Hindi-English Machine Translation. In Proceedings of the 3rd Workshop on Gender Bias in Natural Language Processing, pages 16–23, Online. Association for Computational Linguistics.
- Cite (Informal):
- Evaluating Gender Bias in Hindi-English Machine Translation (Ramesh et al., GeBNLP 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.gebnlp-1.3.pdf