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Gender Bias in Machine Translation

Written for the Research library (claude-opus-5-5) from the paper text. Original — aclanthology.org/2021.tacl-1.51.pdf

Summary

Research on gender bias in machine translation (MT) has grown quickly but remains fragmented. Studies define bias in different ways depending on the languages and subtasks they examine, such as coreference resolution or named entity recognition. Earlier surveys covered only monolingual NLP or were narrow in scope. Savoldi et al. offer the first comprehensive review of the topic. They frame bias in human-centred, sociolinguistic terms: a system is biased when it systematically and unfairly disadvantages some people or groups. The authors draw on linguistics, the social sciences and HCI to explain three things: how different languages encode gender, the sources of bias in MT, and the harms it produces. They then review existing ways of measuring and mitigating bias. The paper identifies gaps in the field: almost all work assumes binary gender, and evaluation happens only in lab settings. It closes with future directions, including model de-biasing, non-binary and intersectional perspectives, and human-in-the-loop research. The framework is meant to give future MT fairness work a shared conceptual basis.

Key points

  • The paper adopts Crawford's (2017) distinction between representational harms (under-representation and stereotyping) and allocational harms. An example of the latter is a woman who must spend extra effort correcting masculine references in a translation of her own biography.
  • Following Stahlberg et al. (2007), languages are grouped into three types: genderless (Finnish, Turkish), notional gender (English, Danish) and grammatical gender (Spanish, Arabic).
  • Saunders et al. (2020) is identified as the only MT study that goes beyond a binary view of gender. The authors point to Indirect Non-binary Language (INL) and Direct Non-binary Language (DNL) as possible ways forward.
  • Proposed directions include:
    • explainability methods to detect spurious cues;
    • down-weighting stereotypical training data;
    • intersectional analyses;
    • involving translators, annotators and more diverse development teams.
  • The paper was published in TACL, vol. 9, pp. 845–874 (2021), by authors from the University of Trento and Fondazione Bruno Kessler.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Natural language processing

Topics: Machine translation

Cited by

145 papers of our library cite it (of the 140673 whose bibliographies we read); 2 of them have a page here, 143 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/tacl_a_00401

@article{savoldi2021gender,
  title = {{Gender Bias in Machine Translation}},
  author = {Savoldi, Beatrice and Gaido, Marco and Bentivogli, Luisa and Negri, Matteo and Turchi, Marco},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2021},
  doi = {10.1162/tacl\_a\_00401},
  url = {https://doi.org/10.1162/tacl_a_00401}
}

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