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A Twin-Candidate Model for Learning-Based Anaphora Resolution

Summary

The paper addresses a weakness of the standard single-candidate approach to learning-based anaphora resolution. That approach judges each potential antecedent independently, so it cannot learn which of several competing candidates should be preferred. The authors propose a twin-candidate model that treats resolution as preference classification. Each instance pairs an anaphor with two candidates, and a classifier decides which candidate is better. The final antecedent comes from ranking candidates through these pairwise judgments, using either an Elimination or a Round Robin scheme. The model works with any discriminative learner. The authors also adapt it to full coreference resolution, where some NPs have no antecedent. They test it on the Automatic Content Extraction (ACE) data in three domains with C5, MaxEnt and SVM learners. For pronoun resolution, the twin-candidate model is significantly more accurate than the single-candidate baseline. For non-pronoun and overall coreference, it matches or beats the baseline. This shows that explicitly modelling competition between candidates improves mention-level resolution.

Key points

  • Evaluation uses the ACE data sets across the NWire, NPaper and BNews domains, with C5, MaxEnt and SVM classifiers. Coreference is scored with Vilain et al.'s (1995) algorithm.
  • For pronominal anaphora, the twin-candidate model improves average accuracy over the single-candidate baseline by up to 4.2%, and the gains are statistically significant.
  • In full coreference with MaxEnt, F-measure rises from 58.3/61.5/62.2 (single-candidate) to 59.2/63.3/63.0 (twin-candidate, Round Robin) for NWire/NPaper/BNews.
  • Earlier pairwise work reported much weaker or hard-to-compare results:
    • Connolly et al. (1997): 55.3% for pronouns and 37.4% for definite NPs.
    • Iida et al. (2003), "tournament model" for Japanese zero anaphora: about 70%.
  • Proposed future work includes richer features, such as statistics-based semantic compatibility, and instance sampling to filter out low-confidence candidates and uninformative non-anaphors.

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

16 papers of our library cite it (of the 140673 whose bibliographies we read); 7 of them have a page here, 9 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/coli.2008.07-004-r2-06-57

@article{yang2008twin,
  title = {{A Twin-Candidate Model for Learning-Based Anaphora Resolution}},
  author = {Yang, Xiaofeng and Su, Jian and Tan, Chew Lim},
  journal = {Computational Linguistics},
  year = {2008},
  doi = {10.1162/coli.2008.07-004-r2-06-57},
  url = {https://doi.org/10.1162/coli.2008.07-004-r2-06-57}
}

References

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