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Entity-Centric Coreference Resolution with Model Stacking

Summary

Coreference systems that score pairs of mentions are accurate and simple to train, but they ignore what is already known about whole entities. They are usually combined with heuristics such as best-first clustering, which can yield incoherent chains like [Hillary Clinton, Clinton, he]. Clark and Manning bridge pairwise and entity-centric approaches through model stacking. They first train two logistic mention pair models: a classification model and a ranking model. The outputs of these models then serve as cluster-level features for an agent that merges clusters agglomeratively. The agent works in easy-first order and is trained by imitation learning based on DAgger. Rather than relying on heuristic notions of good actions, each merge receives an exact cost derived from coreference evaluation metrics, which captures how severe a mistake is. The pairwise scores also order candidate merges and prune unlikely ones, which makes training tractable. On the English part of the CoNLL 2012 Shared Task, the system reaches a CoNLL average F1 of 63.02 and surpasses earlier state-of-the-art systems. This shows that learned, metric-driven clustering over stacked pairwise scores is effective.

Key points

  • The two mention pair models differ only in their training objective:
    • The classification model is optimized over all candidate antecedents.
    • The ranking model uses log-likelihood over the best true and best false antecedent, which keeps scores comparable across mentions.
    • Both use L1 regularization (λ = 0.001) and are optimized with AdaGrad.
  • Features include distance, syntactic, semantic, lexical and rule-based cues, plus a conjunction scheme following Durrett and Klein (2013).
  • Pairwise scores sort merges in easy-first order, and a threshold discards more than 95% of mention pairs before the agent runs.
  • Test-set results on CoNLL 2012 English:
    • This system: 63.02 CoNLL F1.
    • Durrett & Klein (JOINT): 61.71.
    • Björkelund & Kuhn: 61.63.
    • Ma et al.: 61.56.
    • The system has the best B³ F1 (60.44) and CEAFφ4 F1 (56.02).
  • The learned entity-centric agent clearly beats best-first clustering. Combining both pairwise models improves accuracy, while adding classic entity-level features did not help.

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

93 papers of our library cite it (of the 140673 whose bibliographies we read); 44 of them have a page here, 49 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.3115/v1/p15-1136

@inproceedings{clark2015entity,
  title = {{Entity-Centric Coreference Resolution with Model Stacking}},
  author = {Clark, Kevin and Manning, Christopher D.},
  booktitle = {Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  year = {2015},
  doi = {10.3115/v1/p15-1136},
  url = {https://doi.org/10.3115/v1/p15-1136}
}

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