Hierarchical Gated Recurrent Neural Tensor Network for Answer Triggering

摘要：

　　In this paper,we focus on the problem of answer triggering ad-dressed by Yang et al.(2015),which is a critical component for a real-world question answering system.We employ a hierarchical gated recurrent neural tensor(HGRNT)model to capture both the context information and the deep in-teractions between the candidate answers and the question.Our result on F val-ue achieves 42.6%,which surpasses the baseline by over 10%.

关键词： Answer Triggering Question Answering Hierarchical gated recur-rent neural tensor network

作者: Wei Li Yunfang Wu

作者单位: Key Laboratory of Computational Linguistics(Peking University),Ministry of Education School of Electronic Engineering and Computer Science,Peking University

会议类型: 国内会议

会议名称: 第十六届全国计算语言学学术会议暨第五届基于自然标注大数据的自然语言处理国际学术研讨会

会议地点: 南京

会议语种:英文

页码: 1-9

在线出版日期: 2017-10-13（万方平台首次上网日期，不代表论文的发表时间）