Convolutional Deep Neural Networks for Document-Based Question Answering

被引：9

作者：

Fu, Jian ^{[1
]}

Qiu, Xipeng ^{[1
]}

Huang, Xuanjing ^{[1
]}

机构：

[1] Fudan Univ, Sch Comp Sci, 825 Zhangheng Rd, Shanghai, Peoples R China

来源：

NATURAL LANGUAGE UNDERSTANDING AND INTELLIGENT APPLICATIONS (NLPCC 2016) | 2016年 / 10102卷

基金：

国家高技术研究发展计划(863计划); 中国国家自然科学基金;

关键词：

D O I：

10.1007/978-3-319-50496-4_71

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

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 question-answer 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.

引用

页码：790 / 797

页数：8

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