Sentence-Incremental Neural Coreference Resolution

Matt Grenander, Shay B. Cohen, Mark Steedman


Abstract
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 network-based 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 F1 on OntoNotes and 45.5 F1 on CODI-CRAC 2021, 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.
Anthology ID:
2022.emnlp-main.28
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
427–443
Language:
URL:
https://aclanthology.org/2022.emnlp-main.28
DOI:
Bibkey:
Cite (ACL):
Matt Grenander, Shay B. Cohen, and Mark Steedman. 2022. Sentence-Incremental Neural Coreference Resolution. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 427–443, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Sentence-Incremental Neural Coreference Resolution (Grenander et al., EMNLP 2022)
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PDF:
https://preview.aclanthology.org/emnlp-22-ingestion/2022.emnlp-main.28.pdf