@inproceedings{grenander-etal-2022-sentence,
title = "Sentence-Incremental Neural Coreference Resolution",
author = "Grenander, Matt and
Cohen, Shay B. and
Steedman, Mark",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.28",
doi = "10.18653/v1/2022.emnlp-main.28",
pages = "427--443",
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.",
}
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%0 Conference Proceedings
%T Sentence-Incremental Neural Coreference Resolution
%A Grenander, Matt
%A Cohen, Shay B.
%A Steedman, Mark
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F grenander-etal-2022-sentence
%X 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.
%R 10.18653/v1/2022.emnlp-main.28
%U https://aclanthology.org/2022.emnlp-main.28
%U https://doi.org/10.18653/v1/2022.emnlp-main.28
%P 427-443
Markdown (Informal)
[Sentence-Incremental Neural Coreference Resolution](https://aclanthology.org/2022.emnlp-main.28) (Grenander et al., EMNLP 2022)
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.