  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 and leverage
target information 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.
