Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interaction
This addresses the challenge of human-like emotional interaction in dialogue systems, though it is incremental as it builds on existing neural approaches.
The paper tackled the problem of generating emotionally consistent responses in dialogue systems by proposing a unified neural architecture that encodes both semantics and emotions, resulting in improved content coherence and emotion appropriateness compared to state-of-the-art methods.
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.