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Toward Gender-Inclusive Coreference Resolution

Written for the Research library (claude-opus-5-5) from the paper text. Original — aclanthology.org/2020.acl-main.418.pdf

Summary

Coreference systems often treat gender as a fixed binary that maps neatly onto pronouns. Cao and Daumé III show that this assumption causes concrete harms for trans and cis people, both binary and non-binary, such as erased mentions, misgendering and poorer service. They first draw on sociology and sociolinguistics to separate several notions of gender: lexical, referential, grammatical and social. They then examine where bias enters two stages of a machine learning pipeline.

For annotation, they build MAP, a GAP-style corpus without binary constraints. It supports counterfactual ablations that test how crowdworkers on Mechanical Turk rely on gender cues. For modelling, they release GICoref, a fully annotated English coreference corpus of texts written by and about trans people. They use it to evaluate existing systems.

Current systems perform poorly on GICoref and almost entirely miss neopronouns. The authors argue that treating crowd judgments as gold can itself introduce bias, and that experiential experts may need to be involved. The paper offers both a conceptual framework and test resources for gender-inclusive NLP.

Key points

  • Two new English datasets, MAP and GICoref, are released under a BSD license on GitHub, with accompanying datasheets. Part of the GICoref source material comes from Archive of Our Own.
  • Inter-annotator agreement on GICoref, measured with the LEA metric, reaches an F1 of 92%. Most disagreements came from overlooked mentions rather than genuine disputes.
  • The best system scores only about 34% F1 on GICoref. By comparison, the Stanford neural system reaches roughly 60% on the CoNLL-2012 test set. Across systems, precision exceeds recall.
  • Recall for detecting referential pronouns differs sharply by pronoun type:
    • at least 95% for HE/SHE;
    • about 90% for THEY;
    • only about 13% for neopronouns. The Stanford deterministic and Stanford neural systems never detect neopronouns at all.
  • The authors acknowledge several limitations:
    • the study covers English only;
    • the data and gender models have a Western bias, partly offset by including identities such as hijra, muxe and fa'afafine;
    • current systems and metrics cannot handle misgendering or deadnaming.

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

103 papers of our library cite it (of the 140673 whose bibliographies we read); 11 of them have a page here, 92 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2020.acl-main.418 · arXiv 1910.13913

@inproceedings{cao2020toward,
  title = {{Toward Gender-Inclusive Coreference Resolution}},
  author = {Cao, Yang Trista and III, Hal Daumé},
  booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
  year = {2020},
  doi = {10.18653/v1/2020.acl-main.418},
  eprint = {1910.13913},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2020.acl-main.418}
}

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