Beyond Coarse Labels: Fine-Grained Problem Augmentation and Multi-Dimensional Feedback for Emotional Support Conversation (2025)

Abstract: Emotional support conversation systems aim to help users alleviate distress through empathetic dialogue. However, existing ESC datasets often use coarse-grained problem categories, limiting models’ ability to address users’ complex, overlapping challenges. To address this, we propose a generalizable fine-grained problem enhancement method that systematically augments problem types, user scenarios, and profiles, enabling the construction of richer and more diverse ESC corpora. As a demonstration, we construct EmoCare, a large-scale ESC dataset with 2.6K dialogues and 42.8K utterances, expanding problem type coverage from 13 to 45 fine-grained categories. Building on this data augmentation process, we introduce FPEMF, a flexible framework for empathetic dialogue generation, which comprises two modules: fine-grained problem enhancement and multi-dimensional feedback, which can be seamlessly integrated with various backbone models. The multi-dimensional feedback module evaluates responses from four perspectives: emotional understanding, strategy effectiveness, contextual consistency, and topic relevance, guiding models to generate more supportive replies. Experiments show that FPEMF consistently improves both automatic and human evaluation metrics across different models.

## introduction empathy, which is the ability to understand and respond to others' emotions, has become a key focus in dialogue systems research @xcite . as mental health concerns rise globally, scalable emotional support is increasingly needed, yet professional counseling remains inaccessible for many due to cost and resource constraints @xcite have you tried to talk to your kids about that? 😥 talking is great when we are doing it and they all agree. but it never stays this way. the responses generated by existing models figure 1 : an example showing a user facing both work and family pressures. esconv labels this as a "job crisis", which misses the complexity of multiple stressors. and @xcite . this motivates the development of conversational agents capable of providing effective emotional support at scale @xcite . the emotional support conversation (esc) task @xcite and existing models such as kemi @xcite , pal @xcite , and dkpe @xcite have advanced empathetic dialogue by leveraging external knowledge. however, their limited problem categories hinder performance in complex, multi-challenge scenarios, where users often face overlapping stressors. as shown in figure 1 , the user is experiencing the dual pressures of both work and family responsibilities. however, the dataset simply classifies this situation as a "job crisis" problem, reflecting the issue of coarse-grained problem categories. this rough classification fails to capture the unique challenges arising from the overlap of multiple stressors. in addition, the intertwining of multiple sources of stress often leads to responses that lack specificity when generated under a singlestrategy framework. for example, kemi gives a generic suggestion even after the user explains that talking does not solve the problem for long. pal only expresses understanding and shares a similar experience, not providing any practical advice. dkpe repeats the idea of talking but does not address the user's real needs. these responses fail to address the user's nuanced needs and fully support users facing complex and overlapping challenges. this highlights the necessity of multi-dimensional feedback to guide responses toward greater comprehensiveness and accuracy. to address the limitations of coarse-grained problem categories, we propose a systematic data augmentation method. we first collaborate with domain experts to expand the set of problem types from 13 to 45 fine-grained categories. for each type, we automatically generate diverse real-world scenarios and detailed user profiles, resulting in richer and more realistic training data. as a demonstration, we construct emocare, a large-scale esc dataset with 2,574 dialogues and 42,770 utterances, providing a stronger foundation for modeling complex user challenges. we further introduce fpemf, a modular framework for empathetic dialogue generation. its two components: fine-grained problem enhancement and multi-dimensional feedback, which can be flexibly integrated with various backbone models. the enhancement module uses detailed problem types from emocare to better capture users' needs. the feedback module evaluates responses across emotional understanding, strategy effectiveness, contextual consistency, and topic relevance, guiding models to generate more comprehensive and supportive replies. experiments show that fpemf consistently improves performance across different models, especially in complex scenarios. our main contributions are as follows: ## case study ## conclusion in this paper, we present emocare, a large-scale esc dataset with fine-grained problem types and diverse user profiles, and propose fpemf, a novel framework that integrates fine-grained problem enhancement and multi-dimensional feedback for empathetic dialogue generation. extensive experiments show that fpemf consistently improves both automatic and human evaluation metrics across various backbone models, especially in complex scenarios with multiple or overlapping user stressors. these results demonstrate the effectiveness of fpemf in enhancing empathy, relevance, and supportiveness in emotional support dialogue systems. we hope this work provides a valuable resource and methodology for future research in empathetic conversational ai.