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.
Emily Allaway, Shuai Wang, and Miguel Ballesteros. 2021.Sequential Cross-Document Coreference Resolution. InProceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 4659–4671, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
@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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%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
Emily Allaway, Shuai Wang, and Miguel Ballesteros. 2021.Sequential Cross-Document Coreference Resolution. InProceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 4659–4671, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.