Generating Gender Augmented Data for NLP

Nishtha Jain, Maja Popovic, Declan Groves, Eva Vanmassenhove

Research output: Contribution to conferencePaperScientificpeer-review

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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 rewriting 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 (NMT) system trained to 'translate' from one gender alternative to another. Both the automatic and manual analysis of the approach show promising results for automatic generation of gender alternatives for conversational sentences in Spanish.
Original languageEnglish
Number of pages10
Publication statusPublished - 13 Jul 2021
Event3th Workshop on Gender Bias in Natural Language Processing : co-located with ACL-IJCNLP - Bangkok, Thailand
Duration: 5 Aug 20215 Aug 2021


Workshop3th Workshop on Gender Bias in Natural Language Processing
Abbreviated titleGeBNLP
Internet address


  • cs.CL


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