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Cross-document Event Coreference Search: Task, Dataset and Modeling

Summary

Cross-document event coreference systems have usually been asked to link every coreferent mention within a fixed and fairly small document set. The authors argue that many practical needs look different. A reader notices one event and wants every other passage in a huge collection that discusses it. They frame this as Cross-document Coreference Search and build CoreSearch, a Wikipedia-based benchmark. It reorganizes the manually validated clusters of WEC-Eng into queries and positive passages, then hides them among millions of negative "destructor" passages. Because the setup mirrors open-domain question answering, they adapt the DPR retriever–reader pipeline as a baseline, using SpanBERT and boundary tokens around the query mention. Their main model, E2E-Integrated, replaces the reader's passage-selection scoring with a coreference scorer adapted from the end-to-end model of Lee et al. (2017). This improves both re-ranking and detection of the coreferring span. The resource makes event-focused semantic search measurable at realistic scale, and the conversion recipe could be reused for corpora such as HyperCoref.

Key points

  • Dataset composition: CoreSearch merges the validated dev and test portions of WEC-Eng into 522 non-singleton clusters with 3,110 annotated passages, split 237/49/236 clusters across train/dev/test.
  • Distractor corpus: About 2.77M passages in total, roughly 0.92M destructor passages per split.
    • Negatives come from the WEC-Eng train set and from first paragraphs of Wikipedia articles that do not link to the annotated events.
  • Ablation findings:
    • Replacing BERT with SpanBERT produced most of the retriever gain.
    • Span boundary tokens improved results further, mainly helping span detection in the reader.
  • Error analysis: Of 20 sampled top-1 errors, 2 were actually unannotated true coreferences.
    • 10 involved mismatched event arguments.
    • 8 involved contexts too thin to decide coreference, suggesting value in external knowledge and cluster-level reasoning.
  • Availability: The dataset (huggingface: Intel/CoreSearch) and code (github: AlonEirew/CoreSearch) are openly released.

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

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2022.emnlp-main.58 · arXiv 2210.12654

@inproceedings{eirew2022cross,
  title = {{Cross-document Event Coreference Search: Task, Dataset and Modeling}},
  author = {Eirew, Alon and Caciularu, Avi and Dagan, Ido},
  booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing},
  year = {2022},
  doi = {10.18653/v1/2022.emnlp-main.58},
  eprint = {2210.12654},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2022.emnlp-main.58}
}

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