Generating Gender Augmented Data for NLP

Nishtha Jain, Maja Popović, Declan Groves, Eva Vanmassenhove


Abstract
Gender bias is a frequent occurrence in NLP-based applications, especially pronounced in gender-inflected languages. Bias can appear through associations of certain adjectives and animate nouns with the natural gender of referents, but also due to unbalanced grammatical gender frequencies of inflected words. This type of bias becomes more evident in generating conversational utterances where gender is not specified within the sentence, because most current NLP applications still work on a sentence-level context. As a step towards more inclusive NLP, this paper proposes an automatic and generalisable re-writing approach for short conversational sentences. The rewriting method can be applied to sentences that, without extra-sentential context, have multiple equivalent alternatives in terms of gender. The method can be applied both for creating gender balanced outputs as well as for creating gender balanced training data. The proposed approach is based on a neural machine translation system trained to ‘translate’ from one gender alternative to another. Both the automatic and manual analysis of the approach show promising results with respect to the automatic generation of gender alternatives for conversational sentences in Spanish.
Anthology ID:
2021.gebnlp-1.11
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:
93–102
Language:
URL:
https://aclanthology.org/2021.gebnlp-1.11
DOI:
10.18653/v1/2021.gebnlp-1.11
Bibkey:
Cite (ACL):
Nishtha Jain, Maja Popović, Declan Groves, and Eva Vanmassenhove. 2021. Generating Gender Augmented Data for NLP. In Proceedings of the 3rd Workshop on Gender Bias in Natural Language Processing, pages 93–102, Online. Association for Computational Linguistics.
Cite (Informal):
Generating Gender Augmented Data for NLP (Jain et al., GeBNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.gebnlp-1.11.pdf
Optional supplementary material:
 2021.gebnlp-1.11.OptionalSupplementaryMaterial.pdf
Code
 awslabs/sockeye