Answer Sequence Learning with Neural Networks for Answer Selection in Community Question Answering

Xiaoqiang Zhou; Baotian Hu; Qingcai Chen; Buzhou Tang; Xiaolong Wang

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arXiv · 1506.06490

Answer Sequence Learning with Neural Networks for Answer Selection in Community Question Answering

Xiaoqiang Zhou·Baotian Hu·Qingcai Chen·Buzhou Tang·Xiaolong Wang

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Abstract

In this paper, the answer selection problem in community question answering (CQA) is regarded as an answer sequence labeling task, and a novel approach is proposed based on the recurrent architecture for this problem. Our approach applies convolution neural networks (CNNs) to learning the joint representation of question-answer pair firstly, and then uses the joint representation as input of the long short-term memory (LSTM) to learn the answer sequence of a question for labeling the matching quality of each answer. Experiments conducted on the SemEval 2015 CQA dataset shows the effectiveness of our approach.

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BibTeX RIS

Xiaoqiang Zhou, Baotian Hu, Qingcai Chen, Buzhou Tang, Xiaolong Wang. 2015-06-22. Answer Sequence Learning with Neural Networks for Answer Selection in Community Question Answering. https://arxiv.org/abs/1506.06490

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