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Multilingual Coreference Resolution in Multiparty Dialogue

Summary

Most coreference resolution work targets single-author text such as newswire. Conversations with more than two speakers are poorly covered, even though there "you" or "we" may refer to any subset of the people present. Existing resources such as Friends CI label only people and are English-only.

The authors build MMC (Multilingual Multiparty Coreference) from transcripts of Friends and The Big Bang Theory. An ensemble of the Berkeley parser and spaCy proposes candidate mentions. Annotators then link these to antecedents in an adapted interface, covering all entity types rather than just characters. The shows have human-made subtitles in the OpenSubtitles corpus, so the English labels are transferred through word alignment to Chinese and Farsi as silver data. The test sets in those languages are also manually corrected.

Off-the-shelf models trained on earlier datasets such as OntoNotes perform relatively poorly on MMC. Silver data helps both as augmentation and as the sole training source, which simulates zero-shot cross-lingual transfer. The approach offers a cheap route to dialogue coreference data in many languages without in-language experts.

Key points

  • MMC contains 1,222 scenes after filtering: 979 from TBBT (88 episodes) and 243 from Friends (21 episodes), with gold English and projected silver Chinese and Farsi annotations.
  • Correcting the Chinese and Farsi test sets lowers model F1 by only a few points. The OntoNotes-only model drops by 0.85, so the uncorrected silver data remains a usable substitute for development.
  • In a from-scratch annotation check on three Chinese scenes, MUC agreement was 71.84 for silver projections, 78.23 for corrected projections and 87.25 for oracle-alignment projections.
  • Roughly 70% of automatically predicted alignments were judged correct during the correction step.
  • Data, code and the annotation tool are released under the Apache License at github.com/boyuanzheng010/mmc. The paper appeared in TACL, vol. 11 (2023).

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

9 papers of our library cite it (of the 140673 whose bibliographies we read); 1 of them have a page here, 8 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/tacl_a_00581 · arXiv 2208.01307

@article{zheng2023multilingual,
  title = {{Multilingual Coreference Resolution in Multiparty Dialogue}},
  author = {Zheng, Boyuan and Xia, Patrick and Yarmohammadi, Mahsa and Durme, Benjamin Van},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2023},
  doi = {10.1162/tacl\_a\_00581},
  eprint = {2208.01307},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1162/tacl_a_00581}
}

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