  Neural generative models have become popular and achieved promising
performance on short-text conversation tasks. They are generally trained to
build a 1-to-1 mapping from the input post to its output response. However, a
given post is often associated with multiple replies simultaneously in real
applications. Previous research on this task mainly focuses on improving the
relevance and informativeness of the top one generated response for each post.
Very few works study generating multiple accurate and diverse responses for the
same post. In this paper, we propose a novel response generation model, which
considers a set of responses jointly and generates multiple diverse responses
simultaneously. A reinforcement learning algorithm is designed to solve our
model. Experiments on two short-text conversation tasks validate that the
multiple responses generated by our model obtain higher quality and larger
diversity compared with various state-of-the-art generative models.
