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Using neural networks to generate replies in human-computer dialogue systemsis attracting increasing attention over the past few years. However, theperformance is not satisfactory: the neural network tends to generate safe,universally relevant replies which carry little meaning. In this paper, wepropose a content-introducing approach to neural network-based generativedialogue systems. We first use pointwise mutual information (PMI) to predict anoun as a keyword, reflecting the main gist of the reply. We then proposeseq2BF, a "sequence to backward and forward sequences" model, which generates areply containing the given keyword. Experimental results show that our approachsignificantly outperforms traditional sequence-to-sequence models in terms ofhuman evaluation and the entropy measure, and that the predicted keyword canappear at an appropriate position in the reply.

Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation