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Exploiting Document Structures and Cluster Consistencies for Event Coreference Resolution

Summary

The paper addresses within-document event coreference resolution (ECR), which groups event mentions in a text by the real-world event they denote. The authors identify two weaknesses in earlier neural ECR systems. First, they model document-wide context poorly, using sparse hand-crafted features and ignoring how entities and context words relate. Second, they never use agreement between gold and predicted clusters as a training signal. The proposed model, StructECR, encodes text with BERT and builds a document graph whose nodes are context words, event mentions and entity mentions. Edges are weighted by discourse cues (sentence boundaries, coreference, mention spans), syntactic dependencies and semantic similarity. A GCN then enriches the mention representations before end-to-end resolution. Extra loss terms penalize inconsistencies between gold and predicted clusters. StructECR reports state-of-the-art results on KBP benchmarks. Ablations show that every component contributes, and the model transfers better across genres than its baselines. This matters because it shows that explicit document structure and cluster-level supervision improve event-level information extraction.

Key points

  • Encoding: BERT encodes documents in segments of 512 word-pieces. Entity mentions come from Stanford CoreNLP, and the predicted event mentions follow Choubey and Huang (2018) for comparability.
  • Graph edges: five interaction scores (sentence, span, coreference, dependency, semantic-based) are merged into a single adjacency matrix for the GCN. Keeping them as separate graphs ("GraphCombine" ablation) performs worse.
  • Regularization: three cluster-consistency terms (L_inner, L_inter, L_sim) are added to the training loss.
  • Ablations (KBP 2015 dev): the full model reaches 70.57 AVG F1. Every ablated variant drops to between roughly 66.9 and 69.6.
  • Cross-domain (AVG F1, newswire ↔ discussion forum on KBP 2016/2017): StructECR outperforms E2E-Only and Pairwise in every setup (p < 0.01).
    • KBP 2016, NW→DF: 68.19 vs 65.82 vs 60.51.
    • KBP 2017, DF→NW: 65.12 vs 62.52 vs 59.10.

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

12 papers of our library cite it (of the 140673 whose bibliographies we read); 2 of them have a page here, 10 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2021.acl-long.374

@inproceedings{tran2021exploiting,
  title = {{Exploiting Document Structures and Cluster Consistencies for Event Coreference Resolution}},
  author = {Tran, Hieu Minh and Phung, Duy and Nguyen, Thien Huu},
  booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  year = {2021},
  doi = {10.18653/v1/2021.acl-long.374},
  url = {https://doi.org/10.18653/v1/2021.acl-long.374}
}

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