Abstract
Word embeddings learnt from massive text collections have demonstrated significant levels of discriminative biases such as gender, racial or ethnic biases, which in turn bias the down-stream NLP applications that use those word embeddings. Taking gender-bias as a working example, we propose a debiasing method that preserves non-discriminative gender-related information, while removing stereotypical discriminative gender biases from pre-trained word embeddings. Specifically, we consider four types of information: feminine, masculine, gender-neutral and stereotypical, which represent the relationship between gender vs. bias, and propose a debiasing method that (a) preserves the gender-related information in feminine and masculine words, (b) preserves the neutrality in gender-neutral words, and (c) removes the biases from stereotypical words. Experimental results on several previously proposed benchmark datasets show that our proposed method can debias pre-trained word embeddings better than existing SoTA methods proposed for debiasing word embeddings while preserving gender-related but non-discriminative information.- Anthology ID:
- P19-1160
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1641–1650
- Language:
- URL:
- https://aclanthology.org/P19-1160
- DOI:
- 10.18653/v1/P19-1160
- Cite (ACL):
- Masahiro Kaneko and Danushka Bollegala. 2019. Gender-preserving Debiasing for Pre-trained Word Embeddings. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1641–1650, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Gender-preserving Debiasing for Pre-trained Word Embeddings (Kaneko & Bollegala, ACL 2019)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/P19-1160.pdf
- Code
- kanekomasahiro/gp_debias