Knowledge-enhanced Memory Model for Emotional Support Conversation

TL;DR

This paper introduces MODERN, a knowledge-enhanced memory model designed to improve emotional support conversations by addressing emotion variability, response practicality, and strategy modeling, leading to more coherent and effective support.

Contribution

The paper presents a novel memory-enhanced model that incorporates knowledge-enriched context encoding and strategy modeling for emotional support conversations, outperforming existing methods.

Findings

MODERN outperforms state-of-the-art baselines on large-scale datasets.

Knowledge-enriched context encoding improves emotion perception.

Memory-enhanced strategy modeling captures semantic patterns effectively.

Abstract

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.