Bidirectional Long Short-Term Memory with Gated Relevance Network for Paraphrase Identification

摘要：

　　Semantic interaction between text segments,which has been proven to be very useful for detecting the paraphrase relations,is often ignored in the study of paraphrase identification.In this paper,we adopt a neural network model for paraphrase identication,called as bidirectional Long Short-Term Memory-Gated Relevance Network(Bi-LSTM+GRN).According to this model,a gated relevance network is used to capture the semantic interaction between text segments,and then aggregated using a pooling layer to select the most informative interactions.Experiments on the Microsoft Research Paraphrase Corpus(MSRP)benchmark dataset show that this model achieves better performances than hand-crafted feature based approaches as well as previous neural network models.

关键词： Gated Relevance Network Paraphrase Identification LSTM

作者: Yatian Shen Jifan Chen Xuanjing Huang

作者单位: School of Computer Science,Fudan University,825 Zhangheng Road,Shanghai,China

会议类型: 国际会议

会议名称: 第五届自然语言处理与中文计算会议(NLPCC-ICCPOL2016)

会议地点: 昆明

会议语种:英文

页码: 1-12

在线出版日期: 2016-12-02（万方平台首次上网日期，不代表论文的发表时间）