  Using neural networks to generate replies in human-computer dialogue systems
is attracting increasing attention over the past few years. However, the
performance is not satisfactory: the neural network tends to generate safe,
universally relevant replies which carry little meaning. In this paper, we
propose a content-introducing approach to neural network-based generative
dialogue systems. We first use pointwise mutual information (PMI) to predict a
noun as a keyword, reflecting the main gist of the reply. We then propose
seq2BF, a "sequence to backward and forward sequences" model, which generates a
reply containing the given keyword. Experimental results show that our approach
significantly outperforms traditional sequence-to-sequence models in terms of
human evaluation and the entropy measure, and that the predicted keyword can
appear at an appropriate position in the reply.
