STAR : Strategy-Aware Refinement Module in Multitask Learning for Emotional Support Conversations (2025)

Abstract: Effective emotional support in conversation requires strategic decision making, as it involves complex, context-sensitive reasoning tailored to diverse individual needs. The Emotional Support Conversation framework addresses this by organizing interactions into three distinct phases—exploration, comforting, and action—which guide strategy selection during response generation. While multitask learning has been applied to jointly optimize strategy prediction and response generation, it often suffers from task interference due to conflicting learning objectives. To overcome this, we propose the Strategy-Aware Refinement Module (STAR), which disentangles the decoder’s hidden states for each task and selectively fuses them via a dynamic gating mechanism. This design preserves task-specific representations while allowing controlled information exchange between tasks, thus reducing interference. Experimental results demonstrate that STAR effectively reduces task interference and achieves state-of-the-art performance in both strategy prediction and supportive response generation.

## introduction approximately one in ten people worldwide experiences a mental disorder, yet only 1% of the global health workforce is dedicated to mental health care, with the most acute shortages found in developing countries @xcite @xcite @xcite . for instance, while the global average is about 3.96 psychiatrists per 100,000 people, countries such as ethiopia (0.04), nigeria (0.06), pakistan (0.19), and india (0.30) fall drastically below this benchmark @xcite . this stark disparity underscores the urgent need for scalable and accessible forms of support, particularly in lowresource settings where mental health professionals are scarce. emotional support, especially when integrated into community-based and non-specialistdelivery interventions, has emerged as a critical component in addressing this global care gap. to address this shortage, the world health organization (who) introduced the mhgap intervention guide, which equips non-specialist providers in primary care with tools to deliver basic psychosocial interventions, such as structured interviews and problem solving therapy @xcite . the success of such community-based programs is evident in real-world implementations. in zimbabwe, for example, the friendship bench program trained lay health workers to provide emotional support through problem-solving therapy, reducing depression to under 14% after six months @xcite . similarly, in pakistan, lady health workers offering home-based cognitive behavioral techniques reduced postpartum depression to 27% compared to 59% in control groups after one year @xcite . these examples demon-strate that even in the absence of clinical experts, structured and empathetic emotional support can significantly improve mental health outcomes. in response to the global need for scalable mental health solutions, researchers have begun to explore artificial intelligence as a promising tool to provide emotional support, particularly in low-resource environments @xcite . however, effective emotional support is not simply a matter of generating empathetic responses-it requires nuanced understanding, contextual sensitivity, and adherence to structured support strategies @xcite . to meet these complex requirements, recent ai research has turned to multitask learning (mtl) as a foundational framework for emotional support systems. mtl enables ai models to jointly learn multiple interrelated tasks, such as detecting user emotional state, selecting appropriate support strategies, and generating empathy responses. this integrated learning process allows for more contextaware and consistent support delivery. notably, several recent studies have successfully implemented mtl architectures to improve the quality and effectiveness of ai-generated emotional support @xcite @xcite @xcite @xcite . these approaches demonstrate that mtl can be a powerful mechanism for aligning ai responses with structured supportive strategies found in humanled interventions. however, while the mtl approach is designed to leverage shared information across tasks to enhance learning efficiency, it can sometimes lead to adverse effects @xcite . this issue arises due to task interference, where the representational requirements of different tasks may be inherently misaligned @xcite , or when conflicting gradients from multiple tasks disrupt the optimization process during backpropagation @xcite . as a result, instead of facilitating knowledge transfer, mtl can sometimes hinder model performance by introducing conflicts between tasks. to mitigate task interference, various approaches have been proposed, including independent subnets to isolate task-specific representations @xcite , task-specific parameterization to adjust the model capacity per task @xcite , and task grouping to cluster related tasks and reduce negative transfer @xcite . however, despite these advancements, ef- ## experiment in section 4.1, we present a detailed analysis of task interference in mtl-based esc, specifically examining the impact of auxiliary tasks(e.g., strategy prediction, emotion recognition) on the primary task of response generation. we further show that our proposed method successfully mitigates this interference. section 4.2 presents a quantitative performance evaluation in comparison with benchmark models, highlighting the superiority of our approach for multiple evaluation metrics. in section 4.3, we further validate the effectiveness of the proposed method through a comparative analysis with approaches based on large language models. finally, in section 4.4, we assess the appropriateness of emotional support responses using the llm-as-a-judge framework. we perform all training and evaluation on the esconv benchmark dataset @xcite . full dataset descriptions, baselines and implementation details are included in appendix a, appendix b and appendix c. ## conclusion this study proposes the strategy-aware refinement (star) module to address the issue of task interference that arises in multitask learning for emotional support conversations (esc). to alleviate representational conflicts between the distinct tasks of strategy prediction and supportive response generation, star separates the hidden representations of each task and selectively integrates necessary information through a dynamic gating mechanism, thereby promoting effective task alignment. the effectiveness of the proposed star module is empirically validated through both quantitative and qualitative experiments: 1) the gradient similarity between strategy prediction and response generation increased from -0.05 to 0.47 after applying star, confirming enhanced training stability and reduced task conflict. 2) t-sne-based visualization showed clearer cluster boundaries among strategies, indicating a visual improvement in representational separation. 3) when star is integrated into existing esc models (e.g., blenderbot-joint), the f1 score improved by 5.58 points, and consistent performance gains were observed across bleu and rouge metrics. 4) in qualitative evaluations using the llm-as-a-judge framework, star-enhanced models demonstrated comparable or superior response quality to large-scale models such as gpt-4o-mini, underscoring their efficiency and practical competitiveness. these findings suggest that the star module enhances both strategic coherence and emotional appropriateness, while maximizing the effectiveness of multitask learning within a lightweight architecture.