Abstract
Event coreference resolution is an important task of information extraction. Previous work often focused on modeling the sentence structure, ignoring the structure between paragraphs which is also important to event coreference resolution. Moreover, almost all previous work modeled event coreference resolution as a classification task. In this paper, we introduce macro discourse structure to help event coreference resolution through a Relational Graph Convolutional Network (R-GCN), which can take advantage of structure and relations between paragraphs. Moreover, we are the first to introduce an encoder-decoder style generation model to further boost event coreference resolution task. The experimental results on the English KBP2016 and KBP2017 datasets show that our model CGECR (Classification and Generation models for Event Coreference Resolution) outperforms the SOTA baselines.
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Notes
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An event mention refers to a phrase or sentence within which an event is described.
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Acknowledgments
The authors would like to thank the three anonymous reviewers for their comments on this paper. This research was supported by the National Natural Science Foundation of China (Nos. 61836007, 62276177 and 62006167.), and Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).
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Huang, C., Xu, S., He, L., Li, P., Zhu, Q. (2023). Incorporating Generation Method and Discourse Structure to Event Coreference Resolution. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Lecture Notes in Computer Science, vol 13625. Springer, Cham. https://doi.org/10.1007/978-3-031-30111-7_7
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