Sentence-Incremental Neural Coreference Resolution

Matt Grenander, Shay B Cohen, Mark Steedman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

We propose a sentence-incremental neural coreference resolution system which incrementally builds clusters after marking mention boundaries in a shift-reduce method. The system is aimed at bridging two recent approaches at coreference resolution: (1) state-of-the-art-non-incremental models that incur quadratic complexity in document length with high computational cost, and (2) memory networkbased models which operate incrementally but do not generalize beyond pronouns. For comparison, we simulate an incremental setting by constraining non-incremental systems to form partial coreference chains before observing new sentences. In this setting, our system outperforms comparable state-of-the-art methods by 2 F1 on OntoNotes and 6.8 F1 on the CODI-CRAC 2021 corpus. In a conventional coreference setup, our system achieves 76.3 F1on OntoNotes and 45.5 F1 on CODI-CRAC2021, which is comparable to state-of-the-art-baselines. We also analyze variations of our system and show that the degree of incrementality in the encoder has a surprisingly large effect on the resulting performance.
Original languageEnglish
Title of host publicationProceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
EditorsYoav Goldberg, Zornitsa Kozareva, Yue Zhang
Place of PublicationAbu Dhabi
PublisherAssociation for Computational Linguistics (ACL)
Number of pages17
ISBN (Electronic)9781959429418
Publication statusPublished - 2 Feb 2023
EventThe 2022 Conference on Empirical Methods in Natural Language Processing - Abu Dhabi, United Arab Emirates
Duration: 7 Dec 202211 Dec 2022


ConferenceThe 2022 Conference on Empirical Methods in Natural Language Processing
Abbreviated titleEMNLP 2022
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi
Internet address


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