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

Understand

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.