Knowledge-enhanced Memory Model for Emotional Support Conversation
This work addresses the problem of providing effective mental health support through AI conversations, but it appears incremental as it builds on existing methods to improve specific aspects.
The paper tackles challenges in Emotional Support Conversation, including emotion variability and practical response generation, by proposing MODERN, a knowledge-enhanced memory model that achieves superior performance over state-of-the-art baselines on a large-scale dataset.
The prevalence of mental disorders has become a significant issue, leading to the increased focus on Emotional Support Conversation as an effective supplement for mental health support. Existing methods have achieved compelling results, however, they still face three challenges: 1) variability of emotions, 2) practicality of the response, and 3) intricate strategy modeling. To address these challenges, we propose a novel knowledge-enhanced Memory mODEl for emotional suppoRt coNversation (MODERN). Specifically, we first devise a knowledge-enriched dialogue context encoding to perceive the dynamic emotion change of different periods of the conversation for coherent user state modeling and select context-related concepts from ConceptNet for practical response generation. Thereafter, we implement a novel memory-enhanced strategy modeling module to model the semantic patterns behind the strategy categories. Extensive experiments on a widely used large-scale dataset verify the superiority of our model over cutting-edge baselines.