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A Context-Dependent Gated Module for Incorporating Symbolic Semantics into Event Coreference Resolution

Summary

The paper addresses within-document event coreference resolution: grouping event mentions in a text that describe the same real-world event. The authors argue that strong pretrained encoders such as SpanBERT still benefit from symbolic features like event type, modality, polarity, genericity, tense and realis. However, upstream systems predict these features automatically, so they are often wrong, and their usefulness depends on context. The authors propose a Context-Dependent Gated Module (CDGM). It splits each feature-based pair representation into two parts relative to the trigger-based representation: a part parallel to it (redundant) and a part orthogonal to it (new). A sigmoid gate then decides how to mix the two. To teach the gates to handle unreliable inputs, they add a noisy training scheme that randomly swaps feature values during training. On ACE 2005 and KBP 2016, with predicted mentions and features, the full model clearly beats a SpanBERT baseline and naive feature concatenation. It surpasses prior systems on KBP 2016. The work shows a general way to use imperfect symbolic signals alongside contextual embeddings.

Key points

  • Features and tools: ACE 2005 features are type, polarity, modality, genericity and tense; KBP 2016 features are type and realis. Event mentions and subtypes come from OneIE, and the remaining attributes come from a SpanBERT-based joint classifier. Feature accuracy on the test sets ranges from about 0.76 (tense) to 0.99 (polarity).
  • ACE 2005, end-to-end: CDGM + Noise reaches 62.07 CoNLL and 59.76 AVG. The baseline scores 58.93 / 55.78, and simple concatenation scores 57.55 / 54.79, slightly below the baseline. A multi-task alternative reaches only 56.5 AVG. These ACE scores are not directly comparable with earlier work, which used 10-fold cross-validation.
  • KBP 2016, end-to-end: the full model scores 43.55 CoNLL and 40.61 AVG, ahead of E3C (41.97 / 38.66) and the baseline (40.57 / 37.59).
  • Ablations: the gains over concatenation are largest for noisier features, such as modality (+3.73 AVG) and genericity (+3.12 AVG). With ground-truth features, CDGM alone works best (88.30 AVG), and noisy training no longer helps.
  • Code: publicly available at github.com/laituan245/eventcoref.

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

21 papers of our library cite it (of the 140673 whose bibliographies we read); 3 of them have a page here, 18 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2021.naacl-main.274 · arXiv 2104.01697

@inproceedings{lai2021context,
  title = {{A Context-Dependent Gated Module for Incorporating Symbolic Semantics into Event Coreference Resolution}},
  author = {Lai, Tuan and Ji, Heng and Bui, Trung and Tran, Quan Hung and Dernoncourt, Franck and Chang, Walter},
  booktitle = {Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  year = {2021},
  doi = {10.18653/v1/2021.naacl-main.274},
  eprint = {2104.01697},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2021.naacl-main.274}
}

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