I ntention ESC : An Intention-Centered Framework for Enhancing Emotional Support in Dialogue Systems (2025)

Abstract: In emotional support conversations, unclear intentions can lead supporters to employ inappropriate strategies, inadvertently imposing their expectations or solutions on the seeker. Clearly defined intentions are essential for guiding both the supporter’s motivations and the overall emotional support process. In this paper, we propose the Intention-centered Emotional Support Conversation (IntentionESC) framework, which defines the possible intentions of supporters in emotional support conversations, identifies key emotional state aspects for inferring these intentions, and maps them to appropriate support strategies. While Large Language Models (LLMs) excel in text generating, they fundamentally operate as probabilistic models trained on extensive datasets, lacking a true understanding of human thought processes and intentions. To address this limitation, we introduce the Intention CEntric Chain-of-Thought (ICECoT) mechanism. ICECoT enables LLMs to mimic human reasoning by analyzing emotional states, inferring intentions, and selecting suitable support strategies, thereby generating more effective emotional support responses. To train the model with ICECoT and integrate expert knowledge, we design an automated annotation pipeline that produces high-quality training data. Furthermore, we develop a comprehensive evaluation scheme to assess emotional support efficacy and conduct extensive experiments to validate our framework. Our data and code will be publically released to facilitate further research.

## introduction emotional support plays a crucial role in social interactions by helping individuals navigate emotional challenges through understanding and comfort @xcite @xcite figure 1 : illustration of our intentionesc framework and icecot. the icecot mechanism involves four key reasoning steps. guided by the intentionesc framework, the system first analyzes the four key aspects of the emotional state, then infers the supporter's emotional support intention-including goals, motivations, and expectations-which informs strategy selection and enables the generation of effective emotional support responses. @xcite . integrating this capability into dialogue systems enhances their ability to engage in more human-like and empathetic interactions, aligning better with users' expectations and preferences @xcite . however, delivering effective emotional support is a complex process requiring systematic strategies and procedures. drawing on helping skills theory @xcite , we observe that a therapist's intention-the rationale behind selecting specific helping skills and the goal of counseling-plays a pivotal role in effective support. likewise, in emotional support conversations, only when supporters act with a clear intention can they select suitable strategies and provide meaningful emotional support. intention, in this context, refers to a supporter's internal motivations, goals, and expectations when providing support. imagine a scenario where a seeker, after experiencing a major setback such as job loss, expresses emotional distress. if the supporter's intention is to "help the seeker feel understood and comforted," they might adopt strategies like affirmation and reassurance, responding with, "i understand how hard this must be for you; it's a huge blow for anyone." such responses effectively address the seeker's emotional needs. conversely, if the supporter's intention is to "solve the problem quickly", they might provide direct advice like, "it's okay, you can start looking for another job right away." while practical, this approach may overlook the seeker's emotional state, potentially leading to ineffective or even counterproductive interactions. as helping skills theory emphasizes, clarifying intention is essential before selecting a strategy @xcite . while existing research has explored the seekers' intentions in emotional support conversations (esc) @xcite , there has been limited focus on the supporters' intentions. @xcite proposes the esc framework for emotional support conversations, which involves three stages and various support strategies, addressing three subproblems related to emotion states, strategies, and evaluation. however, most studies inspired by this framework have primarily focused on the "what to do" aspect-emotional state modeling and strategy selection-while neglecting the "why" aspect, specifically the underlying intentions driving emotional support. to bridge this gap, we propose the intentioncentered emotional support conversation (intentionesc) framework. this framework defines supporters' intentions, identifies the emotional state preconditions necessary for inferring these intentions, and maps these intentions to appropriate strategies. drawing from counseling practices @xcite , we adapt and refine a list of 12 emotional support intentions suited for emotional support scenarios. by analyzing the situational information embedded in these intentions, we identify four key aspects of emotional state analysis crucial for intention inference. furthermore, by establishing explicit links between intentions and strategies, we outline specific strategies supporters can employ to acomplish each intention. as illustrated in figure 1 , our intentionesc framework provides better structured guidance for effective emotional support. large language models (llms) exhibit advanced conversational capabilities due to extensive pretraining on diverse text corpora, offering new opportunities for esc @xcite . however, llms, being fundamentally probabilistic, generate language based on learned patterns rather than genuine comprehension of human thought and intention. to align llms with the intentionesc framework and enhance their ability to generate emotional support responses, we propose the intention centric chainof-thought (icecot) mechanism. icecot operates in three stages: emotional state @xmath0. the emotional state analysis stage forms the foundation for all subsequent stages by extracting crucial contextual information from the conversation. building on this, the intention inference stage determines the supporters' intentions required to guide the selection of appropriate support strategies and generation of responses. unlike escot @xcite , icecot links emotional state analysis and strategy selection through intention, creating a more coherent and interpretable response generation process. by making intention inference explicit, icecot enhances the transparency and trustworthiness of emotional support models. additionally, we establish an automated annotation pipeline to infuse expert knowledge into data, effectively transferring human insights to llms. the main contributions of our work include: (1) we are the first to focus on supporters' intentions in emotional support conversations and propose the intention-centered emotional support conversation (intentionesc) framework; (2) we introduce icecot, a novel reasoning chain mechanism that integrates emotional state analysis, intention inference, and strategy selection for emotional support response generation, alongside an automated annotation process for generating reasoning chain data; (3) we design a new evaluation framework to assess emotional support dialogue models and conduct extensive experiments demonstrating the effectiveness of intention-driven emotional support. 2 related work emotional support conversation liu et al. (2021) proposes the esconv dataset and several sub-tasks for emotional support conversations, including support strategy selection and strategy-constrained response generation, emotion state modeling, and evaluation of support effectiveness. many studies are based on this framework to explore esc tasks. some work focuses on emotional state modeling. glhg @xcite proposes a global-to-local hierarchical graph network to capture multi-source information, including the global cause that describes the emotional problem and the local intention that implies the current speaker's state. cauesc @xcite investigates emotion causes and the intra/internal effects of emotions, devising an attention mechanism to reason about the effects triggered by emotion causes. escot @xcite proposes an emotional support response inference method that focuses on the emotional process based on how emotions are generated. some research also focuses on support strategy selection and strategy-constrained response generation. @xcite proposes the mixed strategy-aware model (misc), a method for responding by skillfully blending strategies. multiesc @xcite conducts support strategy planning by capturing subtle emotional expressions and causes, anticipating user feedback, and dynamically tracking the user's emotional state. additionally, some studies focus on incorporating commonsense knowledge @xcite and personality @xcite . a key link between emotional modeling and strategy selection lies in the clear understanding of the seeker's contextual state, which guides both the choice of strategy and the generation of responses-the intention. however, existing studies have overlooked the specific intentions behind providing emotional support. ## intentionesc framework in emotional support conversations, intention serves as the guiding force behind effective strategies and responses. this section introduces our in-tentionesc framework, which outlines the possible intentions of supporters, identifies key emotional state aspects necessary for inferring the intentions, and maps them to appropriate support strategies to enhance the quality of emotional support. ## clarify to request or provide further clarification when the seeker is vague, incomplete, confusing, contradictory, or inaudible. ## cathart to relieve tension or unhappiness, give the seeker a chance to express their feelings or discuss problems. ## cognitions to identify maladaptive, illogical, or irrational thoughts or attitudes (e.g., "i must be perfect"). ## feelings to help the seeker recognize, deepen, and accept their feelings, encouraging awareness of hidden emotions. ## insight to encourage understanding of the reasons, dynamics, and unconscious motivations behind thoughts, actions, and feelings, including reactions to others' behaviors. to create a comforting and empathic environment that builds trust and relationships, making the seeker feel accepted, comfortable, and less anxious. ## hope to instill hope for change, supporters should help the seeker, boost their morale, and increase their confidence to make changes. ## self-control to encourage the seeker to develop control over their own thoughts, feelings, behaviors, or impulses, and to take appropriate responsibility for their actions. ## change to enhance adaptive skills, behaviors, or cognitions for dealing with self and others, and to provide new, more adaptive frameworks or assessments that help the seeker view themselves differently. ## give information to educate, give facts, correct misperceptions or misinformation, give reasons for supporter's behavior or procedures. ## response generation mechanism the intentionesc framework provides structured guidance for emotional support, ensuring that re-sponses align with the seeker's needs. building on this foundation, we introduce the intention-centric chain-of-thought (icecot) mechanism, which enables llms to follow a structured reasoning process akin to professional emotional support practices. icecot follows a four-step process: first understanding seeker's emotional state, then identifying the supporter's underlying intention, followed by choosing an appropriate support strategy, and finally producing a well-grounded emotional support response. ## conclusion we propose an intention-centered framework (in-tentionesc) for emotional support conversations, bridging the gap between emotional state modeling and strategy selection to improve the effectiveness of emotional support. we further introduce an emotional support response generation mechanism (icecot) that explicitly models the reasoning process while integrating underlying intentions. additionally, we design a comprehensive evaluation scheme for emotional support and conduct extensive experiments to validate our framework. we hope this work serves as a foundation and inspiration for future research in esc, emphasizing intention-driven response optimization.