A Dual Attentive Neural Network Framework with Community Metadata for Answer Selection

Nowadays the community-based question answering (cQA) sites become popular Web service, which have accumulated millions of questions and their associated answers over time. Thus, the answer selection component plays an important role in a cQA system, which ranks the relevant answers to the given question. With the development of this area, problems of noise prevalence and data sparsity become more tough. In our paper, we consider the task of answer selection from two aspects including deep semantic matching and user community metadata representation. We propose a novel dual attentive neural network framework (DANN) to embed question topics and user network structures for answer selection. The representation of questions and answers are first learned by convolutional neural networks (CNNs). Then the DANN learns interactions of questions and answers, which is guided via user network structures and semantic matching of question topics with double attention. We evaluate the performance of our method on the well-known question answering site Stack exchange. The experiments show that our framework outperforms other state-of-the-art solutions to the problem.