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

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