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Misgendering and Assuming Gender in Machine Translation when Working with Low-Resource Languages

arXiv 2401.13165 ↗First posted 24 Jan 2024cs.CLcited by 0

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Abstract (arXiv)

This chapter focuses on gender-related errors in machine translation (MT) in the context of low-resource languages. We begin by explaining what low-resource languages are, examining the inseparable social and computational factors that create such linguistic hierarchies. We demonstrate through a case study of our mother tongue Bengali, a global language spoken by almost 300 million people but still classified as low-resource, how gender is assumed and inferred in translations to and from the high(est)-resource English when no such information is provided in source texts. We discuss the postcolonial and societal impacts of such errors leading to linguistic erasure and representational harms, and conclude by discussing potential solutions towards uplifting languages by providing them more agency in MT conversations.

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References · 12

  1. 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,290
  2. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 951
  3. 2016Semantics derived automatically from language corpora contain human-like biasescited by 894
  4. 2019Evaluating Gender Bias in Machine Translationcited by 247
  5. 2020Detecting Emergent Intersectional Biases: Contextualized Word Embeddings Contain a Distribution of Human-like Biasescited by 167
  6. 2018Assessing Gender Bias in Machine Translation -- A Case Study with Google Translatecited by 121
  7. 2021Gender Bias in Machine Translationcited by 116
  8. 2019On Measuring Gender Bias in Translation of Gender-neutral Pronounscited by 59
  9. 2023ChatGPT Perpetuates Gender Bias in Machine Translation and Ignores Non-Gendered Pronouns: Findings across Bengali and Five other Low-Resource Languagescited by 33
  10. 2022Markedness in Visual Semantic AIcited by 29
  11. 2021Examining Covert Gender Bias: A Case Study in Turkish and English Machine Translation Modelscited by 15
  12. 2023Enhancing Translation for Indigenous Languages: Experiments with Multilingual Modelscited by 4

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