  Traditional neural language models tend to generate generic replies with poor
logic and no emotion. In this paper, a syntactically constrained
bidirectional-asynchronous approach for emotional conversation generation
(E-SCBA) is proposed to address this issue. In our model, pre-generated emotion
keywords and topic keywords are asynchronously introduced into the process of
decoding. It is much different from most existing methods which generate
replies from the first word to the last. Through experiments, the results
indicate that our approach not only improves the diversity of replies, but
gains a boost on both logic and emotion compared with baselines.
