Sequential Cross-Document Coreference Resolution

• URL: http://arxiv.org/abs/2104.08413v1
• Date: Sat, 17 Apr 2021 00:46:57 GMT
• Title: Sequential Cross-Document Coreference Resolution
• Authors: Emily Allaway, Shuai Wang, and Miguel Ballesteros
• Abstract summary: Cross-document coreference resolution is important for the growing interest in multi-document analysis tasks. We propose a new model that extends the efficient sequential prediction paradigm for coreference resolution to cross-document settings. Our model incrementally composes mentions into cluster representations and predicts links between a mention and the already constructed clusters.
• Score: 14.099694053823765
• License: http://creativecommons.org/licenses/by/4.0/
• 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 provides 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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