Abstract
Answer selection is a very important task in domain question answering. However, because of the word variety between questions and answers, there exists the lexical gap between questions and answers, which is the major challenge in question answer matching. In this work, in order to overcome the lexical gap, we propose an attention based bidirectional gated convolution with neural tensor network (ABiRCNN+NTN), which can improve the representations for both questions and answers and model their interactions with a neural tensor network. We carry out large-scale experiments on answer selection dataset, InsuranceQA and achieve new state-of-the-art results on InsuranceQA dataset. The experimental results demonstrate that our model can effectively capture the complex semantic relations between questions and answers and encode them in a more effective way. The source code of our work can be obtained from https://github.com/paperstudy/AnswerSelection.