  The consistency of a response to a given post at semantic-level and
emotional-level is essential for a dialogue system to deliver human-like
interactions. However, this challenge is not well addressed in the literature,
since most of the approaches neglect the emotional information conveyed by a
post while generating responses. This article addresses this problem by
proposing a unifed end-to-end neural architecture, which is capable of
simultaneously encoding the semantics and the emotions in a post for generating
more intelligent responses with appropriately expressed emotions. Extensive
experiments on real-world data demonstrate that the proposed method outperforms
the state-of-the-art methods in terms of both content coherence and emotion
appropriateness.
