Improving Emotional Support Conversation with Strategy-Intent Inference

The demand for emotional support is increasingly growing. To enhance the effectiveness of models in emotional support conversations, strategy-guided dialogue generation has become a common approach. However, relying solely on strategy keywords is insufficient for the model to grasp the underlying logic of emotional support responses. This paper introduces a Strategy-Intent Inference framework designed to assist supporters in selecting appropriate strategies and clarifying strategy intent, thereby improving the quality of emotional support dialogues. To validate the effectiveness of Strategy-Intent Inference, we applied it to the ESConv dataset, expanding it into an emotional support dialogue dataset that incorporates Strategy-Intent Inference, and trained a dialogue model with this capability on the dataset. Comparative experiments conducted in both in-domain and out-of-domain settings demonstrated improvements in multiple metrics (BLEU and Distinct) compared to existing strategy-enhanced models. These results indicate that Strategy-Intent Inference can effectively enhance the model's emotional support capabilities and generalization performance.