mlsanityData to 7 Oct 2026

Gender Lost In Translation: How Bridging The Gap Between Languages Affects Gender Bias in Zero-Shot Multilingual Translation

arXiv 2305.16935 ↗First posted 26 May 2023cs.CLcited by 3

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Abstract (arXiv)

Neural machine translation (NMT) models often suffer from gender biases that harm users and society at large. In this work, we explore how bridging the gap between languages for which parallel data is not available affects gender bias in multilingual NMT, specifically for zero-shot directions. We evaluate translation between grammatical gender languages which requires preserving the inherent gender information from the source in the target language. We study the effect of encouraging language-agnostic hidden representations on models' ability to preserve gender and compare pivot-based and zero-shot translation regarding the influence of the bridge language (participating in all language pairs during training) on gender preservation. We find that language-agnostic representations mitigate zero-shot models' masculine bias, and with increased levels of gender inflection in the bridge language, pivoting surpasses zero-shot translation regarding fairer gender preservation for speaker-related gender agreement.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 11 of its 18 references that are in the map. First posted on arXiv 26 May 2023.

Builds on

11 of 18 references.

References · 11

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,898
  3. 2018A Call for Clarity in Reporting BLEU Scorescited by 1,790
  4. 2021Gender Bias in Machine Translationcited by 116
  5. 2019Getting Gender Right in Neural Machine Translationcited by 98
  6. 2020Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problemcited by 86
  7. 2019The Missing Ingredient in Zero-Shot Neural Machine Translationcited by 59
  8. 2020Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpuscited by 57
  9. 2020Improving Zero-Shot Translation by Disentangling Positional Informationcited by 35
  10. 2019Improving Zero-shot Translation with Language-Independent Constraintscited by 33
  11. 2019Filling Gender & Number Gaps in Neural Machine Translation with Black-box Context Injectioncited by 27

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Cited by year

Citing papers per year: 2023: 1; 2024: 2.

3 papers.

  1. 2024What the Harm? Quantifying the Tangible Impact of Gender Bias in Machine Translation with a Human-centered Studycited by 15
  2. 2023Gender Inflected or Bias Inflicted: On Using Grammatical Gender Cues for Bias Evaluation in Machine Translationcited by 4
  3. 2024Current State-of-the-Art of Bias Detection and Mitigation in Machine Translation for African and European Languages: a Reviewcited by 1