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Contrastive Representation Learning for Cross-Document Coreference Resolution of Events and Entities

Summary

The paper addresses cross-document (CD) coreference resolution of events and entities, where the goal is to group mentions from different documents that refer to the same thing. Strong earlier systems scored every pair of mentions with a transformer cross-encoder, so their cost grows quadratically with the number of mentions. The authors replace pairwise scoring with contrastive representation learning. A Siamese network fine-tunes RoBERTa-large to produce one 2048-dimensional embedding per mention, which means inference needs only n transformer passes.

Each embedding combines two parts:

  • a summed representation of the mention span, marked with [E]/[/E] tags;
  • the CLS vector of an input that joins the document's first two sentences (usually headline and dateline) with the mention's sentence.

Agglomerative clustering then groups the embeddings into coreference chains. The model was evaluated on ECB+ using gold mentions. It reaches state-of-the-art results on several key metrics and stays competitive with costlier cross-encoders. It also runs faster than a comparable linear-time baseline. This matters for large or growing document collections, such as multi-document summarization or multi-hop QA.

Key points

  • Data split: ECB+ follows the Cybulska and Vossen (2015) setup. Topics 1–35 are used for training, with topics 2, 5, 12, 18, 21, 23, 34 and 35 held out for development. Topics 36–45 form the test set, which contains 1780 event mentions and 2055 entity mentions.
  • Speed: Inference on the ECB+ event test set took about 15 seconds. The RoBERTa-large model of Cattan et al. (2021a,b), which is also linear in transformer passes, needed about 60 seconds under similar settings.
  • Encoder ablations: RoBERTa-large beat BERT-large by about 5 CoNLL F1 on average and BERT-base by about 7.2.
  • Component ablations: Document context added about 5.4 CoNLL F1, the mention representation about 2.8, and the CLS embedding about 1.3.
  • Other findings: On the WEC (Wikipedia Events Coreference) dataset, the model scored 89.3 CoNLL F1 against a benchmark of 62.3. Using TextRank-selected sentences as context instead of the headline heuristic lowered event and entity scores by 4.6 and 3.7 CoNLL F1 respectively.

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

7 papers of our library cite it (of the 140673 whose bibliographies we read); 2 of them have a page here, 5 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2022.naacl-main.267 · arXiv 2205.11438

@inproceedings{hsu2022contrastive,
  title = {{Contrastive Representation Learning for Cross-Document Coreference Resolution of Events and Entities}},
  author = {Hsu, Benjamin and Horwood, Graham},
  booktitle = {Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  year = {2022},
  doi = {10.18653/v1/2022.naacl-main.267},
  eprint = {2205.11438},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2022.naacl-main.267}
}

References

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