Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies

Sunipa Dev, Masoud Monajatipoor, Anaelia Ovalle, Arjun Subramonian, Jeff Phillips, Kai-Wei Chang


Abstract
Gender is widely discussed in the context of language tasks and when examining the stereotypes propagated by language models. However, current discussions primarily treat gender as binary, which can perpetuate harms such as the cyclical erasure of non-binary gender identities. These harms are driven by model and dataset biases, which are consequences of the non-recognition and lack of understanding of non-binary genders in society. In this paper, we explain the complexity of gender and language around it, and survey non-binary persons to understand harms associated with the treatment of gender as binary in English language technologies. We also detail how current language representations (e.g., GloVe, BERT) capture and perpetuate these harms and related challenges that need to be acknowledged and addressed for representations to equitably encode gender information.
Anthology ID:
2021.emnlp-main.150
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Editors:
Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1968–1994
Language:
URL:
https://aclanthology.org/2021.emnlp-main.150
DOI:
10.18653/v1/2021.emnlp-main.150
Bibkey:
Cite (ACL):
Sunipa Dev, Masoud Monajatipoor, Anaelia Ovalle, Arjun Subramonian, Jeff Phillips, and Kai-Wei Chang. 2021. Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1968–1994, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies (Dev et al., EMNLP 2021)
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