Generating Gender Augmented Data for NLP

TL;DR

This paper presents a neural machine translation-based method to automatically generate gender-balanced conversational data in Spanish, addressing gender bias issues in NLP applications, especially for gender-inflected languages.

Contribution

It introduces a novel NMT approach for rewriting sentences to produce gender alternatives, enhancing inclusivity and balancing gender representation in NLP datasets.

Findings

Effective automatic generation of gender alternatives in Spanish

Promising results in reducing gender bias in conversational NLP

Applicable to creating balanced training data for NLP models

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 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.