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

A Syntactically Constrained Bidirectional-Asynchronous Approach for Emotional Conversation Generation