标题：A Multi-granularity Neural Network for Answer Sentence Selection
作者：Zhang, Chenggong ;Zhang, Weijuan ;Zha, Daren ;Ren, Pengjie ;Mu, Nan
作者机构：[Zhang, Chenggong ;Zhang, Weijuan ;Zha, Daren ;Mu, Nan ] State Key Laboratory of Information Security, Institute of Information Engineering, Chinese A 更多
会议名称：2019 International Joint Conference on Neural Networks, IJCNN 2019
会议日期：14 July 2019 through 19 July 2019
来源：Proceedings of the International Joint Conference on Neural Networks
摘要：In open-domain question answering system, the granularities of the answers vary with different types of questions. For example, for the questions asking about locations (Location type questions), their answers are usually short phrases. While for the questions asking about reasons (Description type questions), their answers are usually long clauses or sentences. This insight can be used to improve the performance of answer sentence selection, which is a crucial component of the open-domain QA system. In this paper, we propose a novel Multi-Granularity Neural Network (MGNN) model to better evaluate the semantic matching of questions and answers. First, MGNN has three classes of channels with each computing the similarity of question and answer pairs from one of the three granularity levels: clause level, phrase level and ngram level. Then, MGNN uses a parametrization weighting scheme which considers question types to combine these different granularity channels. We carry out experiments on a public available benchmark dataset for question answering. Empirical results show that our method outperforms state-of-the-art methods. © 2019 IEEE.