Think Twice: A Human-like Two-stage Conversational Agent for Emotional Response Generation

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 the rare large-scale emotion-annotated dialogue corpus. Inspired by the "think twice" behavior in human intelligent 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 Empathet-icDialogues datasets demonstrate that the proposed conversational agent outperforms the compared models in the emotion generation and maintains the semantic performance in the automatic and human evaluations.