  Towards human-like dialogue systems, current emotional dialogue approaches
jointly model emotion and semantics with a unified neural network. This
strategy tends to generate safe responses due to the mutual restriction between
emotion and semantics, and requires rare emotion-annotated large-scale dialogue
corpus. Inspired by the "think twice" behavior in human dialogue, we propose a
two-stage conversational agent for the generation of emotional dialogue.
Firstly, a dialogue model trained without the emotion-annotated dialogue corpus
generates a prototype response that meets the contextual semantics. Secondly,
the first-stage prototype is modified by a controllable emotion refiner with
the empathy hypothesis. Experimental results on the DailyDialog and
EmpatheticDialogues datasets demonstrate that the proposed conversational
outperforms the comparison models in emotion generation and maintains the
semantic performance in automatic and human evaluations.
