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Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias Throughout the Machine Learning Lifecycle*

Summary

Coreference systems must make assumptions about the people they track, which can encode gender bias. Earlier work mostly measured binary HE/SHE stereotyping. This paper asks how bias against binary and non-binary trans and cis people enters such systems. The authors draw on sociology and sociolinguistics to replace the "folk" view of gender (two fixed categories that map neatly onto pronouns) with finer distinctions. They then follow an adapted version of Vaughan and Wallach's (2019) machine learning lifecycle, reviewing existing corpora, annotation guidelines, model design and evaluation practices. They also build two new English data sets. One probes bias in crowd annotation through ablation and counterfactual edits. The other tests existing coreference systems on texts by or about trans people. The analysis shows that systems built on simplified gender assumptions underperform for these users, reinforce stereotypes and misrepresent who appears in text. This matters because coreference sits inside search, grammar checking, information extraction and machine translation, where these errors can cause misgendering and erasure.

Key points

  • The paper breaks "gendered words" into four categories: lexical, referential, grammatical and social gender. It treats pronouns (SHE, HE, singular THEY, neopronouns like ZE/HIR and XEY/XEM) directly instead of mapping them to gender labels.
  • It analyzes six of the eight stages of an adapted lifecycle: task definition, data collection, labeling, model definition, testing and feedback.
  • It introduces two data sets:
    • MAP is GAP-like but drops binary gender constraints, which allows counterfactual manipulation for studying annotator bias.
    • GICoref is a fully annotated coreference corpus about non-binary and binary trans people, including some non-Western gender expressions.
  • The authors make each data set publicly available with an accompanying datasheet, licensed under BSD terms.
  • The work extends Cao and Daumé III (ACL 2020). It is limited to English, which lacks grammatical gender, and the authors acknowledge a largely Western framing.

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: Linguistics & languages

Topics: Coreference resolution

Cited by

24 papers of our library cite it (of the 140673 whose bibliographies we read); 2 of them have a page here, 22 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/coli_a_00413

@article{cao2021toward,
  title = {{Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias Throughout the Machine Learning Lifecycle*}},
  author = {Cao, Yang Trista and III, Hal Daumé},
  journal = {Computational Linguistics},
  year = {2021},
  doi = {10.1162/coli\_a\_00413},
  url = {https://doi.org/10.1162/coli_a_00413}
}

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