@inproceedings{allaway-etal-2021-sequential,
title = "Sequential Cross-Document Coreference Resolution",
author = "Allaway, Emily and
Wang, Shuai and
Ballesteros, Miguel",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.382",
doi = "10.18653/v1/2021.emnlp-main.382",
pages = "4659--4671",
abstract = "Relating entities and events in text is a key component of natural language understanding. Cross-document coreference resolution, in particular, is important for the growing interest in multi-document analysis tasks. In this work we propose a new model that extends the efficient sequential prediction paradigm for coreference resolution to cross-document settings and achieves competitive results for both entity and event coreference while providing strong evidence of the efficacy of both sequential models and higher-order inference in cross-document settings. Our model incrementally composes mentions into cluster representations and predicts links between a mention and the already constructed clusters, approximating a higher-order model. In addition, we conduct extensive ablation studies that provide new insights into the importance of various inputs and representation types in coreference.",
}
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<abstract>Relating entities and events in text is a key component of natural language understanding. Cross-document coreference resolution, in particular, is important for the growing interest in multi-document analysis tasks. In this work we propose a new model that extends the efficient sequential prediction paradigm for coreference resolution to cross-document settings and achieves competitive results for both entity and event coreference while providing strong evidence of the efficacy of both sequential models and higher-order inference in cross-document settings. Our model incrementally composes mentions into cluster representations and predicts links between a mention and the already constructed clusters, approximating a higher-order model. In addition, we conduct extensive ablation studies that provide new insights into the importance of various inputs and representation types in coreference.</abstract>
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%0 Conference Proceedings
%T Sequential Cross-Document Coreference Resolution
%A Allaway, Emily
%A Wang, Shuai
%A Ballesteros, Miguel
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F allaway-etal-2021-sequential
%X Relating entities and events in text is a key component of natural language understanding. Cross-document coreference resolution, in particular, is important for the growing interest in multi-document analysis tasks. In this work we propose a new model that extends the efficient sequential prediction paradigm for coreference resolution to cross-document settings and achieves competitive results for both entity and event coreference while providing strong evidence of the efficacy of both sequential models and higher-order inference in cross-document settings. Our model incrementally composes mentions into cluster representations and predicts links between a mention and the already constructed clusters, approximating a higher-order model. In addition, we conduct extensive ablation studies that provide new insights into the importance of various inputs and representation types in coreference.
%R 10.18653/v1/2021.emnlp-main.382
%U https://aclanthology.org/2021.emnlp-main.382
%U https://doi.org/10.18653/v1/2021.emnlp-main.382
%P 4659-4671
Markdown (Informal)
[Sequential Cross-Document Coreference Resolution](https://aclanthology.org/2021.emnlp-main.382) (Allaway et al., EMNLP 2021)
ACL
- Emily Allaway, Shuai Wang, and Miguel Ballesteros. 2021. Sequential Cross-Document Coreference Resolution. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 4659–4671, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.