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 short, safe universal replies which carry little meaning. In this paper, we propose a contentintroducing approach to neural network-based generative dialogue systems. We first use pointwise mutial information (PMI) to predict a noun as a keyword, reflecting the main topic 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-tosequence models in terms of human evaluation and the entropy measure. 1
REF leveraged the Pointwise Mutual Information (PMI) to predict a keyword and presented the seq2BF framework to generate a reply containing the given keywords.
Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation
ArXiv
ArXiv
Computer Science