Target Guided Emotion Aware Chat Machine
This addresses the challenge of human-like interactions in dialogue systems, though it appears incremental as it builds on existing neural approaches.
The paper tackled the problem of generating responses that are consistent both semantically and emotionally in dialogue systems, and demonstrated that their proposed method outperforms state-of-the-art methods in content coherence and emotion appropriateness.
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.