Gender Bias in Masked Language Models for Multiple Languages

Masahiro Kaneko, Aizhan Imankulova, Danushka Bollegala, Naoaki Okazaki


Abstract
Masked Language Models (MLMs) pre-trained by predicting masked tokens on large corpora have been used successfully in natural language processing tasks for a variety of languages.Unfortunately, it was reported that MLMs also learn discriminative biases regarding attributes such as gender and race.Because most studies have focused on MLMs in English, the bias of MLMs in other languages has rarely been investigated.Manual annotation of evaluation data for languages other than English has been challenging due to the cost and difficulty in recruiting annotators.Moreover, the existing bias evaluation methods require the stereotypical sentence pairs consisting of the same context with attribute words (e.g. He/She is a nurse).We propose Multilingual Bias Evaluation (MBE) score, to evaluate bias in various languages using only English attribute word lists and parallel corpora between the target language and English without requiring manually annotated data.We evaluated MLMs in eight languages using the MBE and confirmed that gender-related biases are encoded in MLMs for all those languages.We manually created datasets for gender bias in Japanese and Russian to evaluate the validity of the MBE.The results show that the bias scores reported by the MBE significantly correlates with that computed from the above manually created datasets and the existing English datasets for gender bias.
Anthology ID:
2022.naacl-main.197
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2740–2750
Language:
URL:
https://aclanthology.org/2022.naacl-main.197
DOI:
10.18653/v1/2022.naacl-main.197
Bibkey:
Cite (ACL):
Masahiro Kaneko, Aizhan Imankulova, Danushka Bollegala, and Naoaki Okazaki. 2022. Gender Bias in Masked Language Models for Multiple Languages. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2740–2750, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Gender Bias in Masked Language Models for Multiple Languages (Kaneko et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.197.pdf
Code
 kanekomasahiro/bias_eval_in_multiple_mlm