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Multi-Sentence Argument Linking

Written for the Research library (claude-opus-5-5) from the paper text. Original — aclanthology.org/2020.acl-main.718.pdf

Summary

News reports often spread an event's participants across several sentences, yet most annotated corpora mark arguments only within the trigger's own sentence, which favours sentence-level SRL. Ebner et al. treat the problem as argument linking. An event trigger opens a set of roles, and each role must be connected to an explicit text span anywhere in the nearby document, which makes the task closer to coreference resolution. Earlier cross-sentence corpora were too small for neural training, so the authors crowdsourced RAMS (Roles Across Multiple Sentences). It draws on English news articles selected via Reddit and uses the AIDA Phase 1 event ontology. Annotators picked role fillers inside a five-sentence window centred on the trigger. A span-selection model, building on recent SRL and coreference work, scores 68.3 F1 on RAMS and 73.3 F1 when the event type is given, outperforming strong baselines. It also adapts well to BNB, GVDB and AIDA-1. This work makes document-level event understanding trainable at scale.

Key points

  • RAMS has 9,124 annotated events in 3,993 documents with 21,237 arguments. It covers all 139 event types and 65 roles of the AIDA Phase 1 ontology, while AIDA-1 Practice covers only 88 types.
  • 82% of RAMS arguments appear in the trigger's sentence. In AIDA-1, 38.1% of events have at least one argument outside it, and 90% of arguments fall within a 5-sentence window.
  • Inter-annotator agreement on exact span boundaries is 55.3%. This rises to 69.9% when a one-token difference is allowed.
  • Pre-training on RAMS raises English AIDA-1 Eval F1 from 19.1 to 24.4 with greedy decoding and from 18.2 to 24.8 with TCD.
  • On Beyond NomBank with document-disjoint splits, the model reaches 76.6 F1 on test. On the Gun Violence Database it substantially improves slot-filling results, though these are not directly comparable to earlier work.

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: Natural language processing

Cited by

124 papers of our library cite it (of the 140673 whose bibliographies we read); 2 of them have a page here, 122 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2020.acl-main.718

@inproceedings{ebner2020multi,
  title = {{Multi-Sentence Argument Linking}},
  author = {Ebner, Seth and Xia, Patrick and Culkin, Ryan and Rawlins, Kyle and Durme, Benjamin Van},
  booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
  year = {2020},
  doi = {10.18653/v1/2020.acl-main.718},
  url = {https://doi.org/10.18653/v1/2020.acl-main.718}
}

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