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.
2020, Vol. 41 
