Employing Multiple Decomposable Attention Networks to Resolve Event Coreference

摘要：

　　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 documentlevel 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.

关键词： Event coreference Decomposable Attention Network Global inference

作者: Jie Fang Peifeng Li Guodong Zhou

作者单位: School of Computer Science and Technology,Soochow University,Suzhou,China

会议类型: 国际会议

会议名称: 2018自然语言处理与中文计算国际会议(NLPCC2018)

会议地点: 呼和浩特

会议语种:英文

页码: 246-256

在线出版日期: 2018-08-26（万方平台首次上网日期，不代表论文的发表时间）