Knowledge-enhanced Memory Model for Emotional Support Conversation
Mengzhao Jia Qianglong Chen Liqiang Jing Dawei Fu Renyu Li
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.