Convolutional Deep Neural Networks for Document-based Question Answering

摘要：

　　Document-based Question Answering aims to compute the similarity or relevance between two texts: question and answer.It is a typical and core task and considered as a touchstone of natural language understanding.In this article,we present a convolutional neural network based architecture to learn feature representations of each questionanswer pair and compute its match score.By taking the interaction and attention between question and answer into consideration,as well as word overlap indices,the empirical study on Chinese Open-Domain Question Answering(DBQA)Task(document-based)demonstrates the efficacy of the proposed model,which achieves the best result on NLPCC-ICCPOL 2016 Shared Task on DBQA.

作者: Jian Fu Xipeng Qiu 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: 1-9

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）