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:
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.emnlp-main.28.pdf