Hybrid Neural Network Model for Community Question/Answer Matching
ZHANG Yan-kun,CHEN Yu-zhong,LIU Zhang-hui
(College of Mathematics and Computer Sciences,Fuzhou University,Fuzhou 350116,China)(Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing,Fuzhou 350116,China)
Abstract:Question and answer matching is an important and challenging task of community question answering(CQA).In this paper,we propose a hybrid neural network model for community question/answer matching.For question and answer pairs,a hybrid model of fusion convolution neural network(CNN)and bi-directional long short-term memory network(Bi-LSTM)is proposed to learn the semantic information of question and answer pair and the contextual relevance information of the question and answer sequence.According to the historical answer of the user,a user-question modeling method based on multi-dimensional attention mechanism is proposed to learn the correlation information between the user and the question.The experimental results on SemEval-2015 CQA dataset show that the proposed algorithm can effectively improve the accuracy of CQA matching compared with the existing CQA matching algorithms.