A relation-aware representation approach for the question matching system

Online question matching is the process of comparing user queries with system questions to find appropriate answers. This task has become increasingly important with the popularity of knowledge sharing social networks in product search and intelligent Q &A in customer service. Many previous studies have focused on designing complex semantic structures through the questions themselves. In fact, the online user’s queries accumulate a large number of similar sentences, which have been grouped by semantics in the retrieval system. However, how to use these sentences to enhance the understanding of system questions is rarely studied. In this paper, we propose a novel Relation-aware Semantic Enhancement Network (RSEN) model. Specifically, we leverage the labels of the history records to identify different semantically related sentences. Then, we construct an expanded relation network to integrate the representation of different semantic relations. Furthermore, we interact we integrate the features of the system question with the semantically related sentences to augment the semantic information. Finally, we evaluate our proposed RSEN on two publicly available datasets. The results demonstrate the effectiveness of our proposed RSEN method compared to the advanced baselines.