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Employing Multiple Decomposable Attention Networks to Resolve Event Coreference

  • Jie Fang
  • Peifeng LiEmail author
  • Guodong Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11109)

Abstract

Event coreference resolution is a challenging NLP task due to this task needs to understand the semantics of events. Different with most previous studies used probability-based or graph-based models, this paper introduces a novel neural network, MDAN (Multiple Decomposable Attention Networks), to resolve document-level event coreference from different views, i.e., event mention, event arguments and trigger context. Moreover, it applies a document-level global inference mechanism to further resolve the coreference chains. The experimental results on two popular datasets ACE and TAC-KBP illustrate that our model outperforms the two state-of-the-art baselines.

Keywords

Event coreference Decomposable Attention Network Global inference 

Notes

Acknowledgments

The authors would like to thank three anonymous reviewers for their comments on this paper. This research was supported by the National Natural Science Foundation of China under Grant Nos. 61772354, 61773276 and 61472265, and was also supported by the Strategic Pioneer Research Projects of Defense Science and Technology under Grant No. 17-ZLXDXX-02-06-02-04.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Computer Science and TechnologySoochow UniversitySuzhouChina

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