Hierarchical Variational Memory Network for Dialogue Generation
Dialogue systems help various real applications interact with humans in an intelligent natural way. In dialogue systems, the task of dialogue generation aims to generate utterances given previous utterances as contexts. Among various spectrums of dialogue generation approaches, end to end neural generation models have received an increase of attention. These end to end neural generation models are capable of generating natural sounding sentences with a unified neural encoder decoder network structure. The end to end structure sequentially encodes each word in an input context and generates the response word by word deterministically during decoding. However, lack of variation and limited ability in capturing long term dependencies between utterances still challenge existing approaches. In this paper, we propose a novel hierarchical variational memory network (HVMN) by adding the hierarchical structure and the variational memory network into a neural encoder decoder network. By emulating human to human dialogues, our proposed method can capture both the high level abstract variations and long term memories during dialogue tracking, which enables the random access of relevant dialogue histories. Extensive experiments conducted on three large real world datasets verify a significant improvement of our proposed model against state of the art baselines for dialogue generation.
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