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Does Context Help Mitigate Gender Bias in Neural Machine Translation?

arXiv 2406.12364 ↗First posted 18 Jun 2024cs.CLcited by 0

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Abstract (arXiv)

Neural Machine Translation models tend to perpetuate gender bias present in their training data distribution. Context-aware models have been previously suggested as a means to mitigate this type of bias. In this work, we examine this claim by analysing in detail the translation of stereotypical professions in English to German, and translation with non-informative context in Basque to Spanish. Our results show that, although context-aware models can significantly enhance translation accuracy for feminine terms, they can still maintain or even amplify gender bias. These results highlight the need for more fine-grained approaches to bias mitigation in Neural Machine Translation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 20 references that are in the map. First posted on arXiv 18 Jun 2024.

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References · 13

  1. 2017Attention Is All You Needcited by 60,361
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2019Evaluating Gender Bias in Machine Translationcited by 249
  4. 2018Marian: Fast Neural Machine Translation in C++cited by 203
  5. 2019Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphologycited by 200
  6. 2018Assessing Gender Bias in Machine Translation -- A Case Study with Google Translatecited by 121
  7. 2019Getting Gender Right in Neural Machine Translationcited by 98
  8. 2017Neural Machine Translation with Extended Contextcited by 95
  9. 2017Evaluating Discourse Phenomena in Neural Machine Translationcited by 87
  10. 2020Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problemcited by 86
  11. 2022MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translationcited by 38
  12. 2021Extending Challenge Sets to Uncover Gender Bias in Machine Translation: Impact of Stereotypical Verbs and Adjectivescited by 14
1 more
  1. 2022How sensitive are translation systems to extra contexts? Mitigating gender bias in Neural Machine Translation models through relevant contextscited by 10

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