  We aim to overcome the lack of diversity in responses of current dialogue
systems and to develop a dialogue system that is engaging as a conversational
partner. We propose a generator-evaluator model that evaluates multiple
responses generated by a response generator and selects the best response by an
evaluator. By generating multiple responses, we obtain diverse responses. We
conduct human evaluations to compare the output of the proposed system with
that of a baseline system. The results of the human evaluations showed that the
proposed system's responses were often judged to be better than the baseline
system's, and indicated the effectiveness of the proposed method.
