Multi-Turn Response Selection for Chatbots With Hierarchical Aggregation Network of Multi-Representation

Abstract

Matching an appropriate response with its multi-turn context is a crucial challenge in retrieval-based chatbots. Current studies construct multiple representations of context and response to facilitate response selection, but they use these representations in isolation and ignore the relationships among representations. To address these problems, we propose a hierarchical aggregation network of multi-representation (HAMR) to leverage abundant representations sufficiently and enhance valuable information. First, we employ bidirectional recurrent neural networks (BiRNN) to extract syntactic and semantic representations of sentences and use a self-aggregation mechanism to combine these representations. Second, we design a matching aggregation mechanism for fusing different matching information between each utterance in context and response, which is generated by an attention mechanism. By considering the candidate response as the real part of the context, we try to integrate all of them in chronological order and then accumulate the vectors to calculate the final matching degree. An extensive empirical study on two multi-turn response selection data sets indicates that our proposed model achieves a new state-of-the-art result.