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Generating gender augmented data for NLP

Jain, Nishtha, Popović, Maja orcid logoORCID: 0000-0001-8234-8745, Groves, Declan and Vanmassenhove, Eva orcid logoORCID: 0000-0003-1162-820X (2021) Generating gender augmented data for NLP. In: 3rd Workshop on Gender Bias in Natural Language Processing, 5 Aug 2021, Online.

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.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Refereed:Yes
Subjects:Computer Science > Machine learning
Humanities > Language
Humanities > Linguistics
Social Sciences > Gender
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: Proceedings of the 3rd Workshop on Gender Bias in Natural Language Processing. . Association for Computational Linguistics (ACL).
Publisher:Association for Computational Linguistics (ACL)
Official URL:https://doi.org/10.18653/v1/2021.gebnlp-1.11
Copyright Information:© 2021 Association for Computational Linguistics
ID Code:28360
Deposited On:24 May 2023 09:34 by Maja Popovic . Last Modified 24 May 2023 09:34
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