Dense Semantic Matching Network for Multi-turn Conversation

Mining the semantic information in the text to model the multi-turn conversation has attracted great interests. Previous models ignore the capturing and utilizing of the matching patterns at different levels, which causes the loss of the valuable information for the calculation of the matching degree. To address the problem, we propose a dense semantic matching network (DMN). Given a context-response pair, DMN first constructs semantic representations for the response candidate and each utterance in the context. Then, DMN models the interaction between the response candidate and each utterance in the context to generate interactive matrices. By processing the interactive matrices with dense convolutional blocks, the hierarchical matching patterns are generated for each utterance-response pair. The matching patterns of all the utterance-response pairs are finally accumulated in chronological order with bidirectional long short-term memory network. The final matching score of the response candidate and the multi-turn context is finally calculated. We evaluate the performance of our network on the benchmark dataset. The results show that our network yields a significant performance gain compared with other methods.