Papers with ESC
Evaluating Instructively Generated Statement by Large Language Models for Directional Event Causality Identification (2025.findings-acl)
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| Challenge: | Existing studies focus on identifying existence of causality between two event mentions, but the direction of causalities is crucial for understanding the causal relation. |
| Approach: | They propose to instruct a GLM to generate causality statements and identify directional event causality by evaluating the generated statements. |
| Outcome: | The proposed method significantly outperforms state-of-the-art methods even with fewer training data. |
Flexible Thinking for Multimodal Emotional Support Conversation via Reinforcement Learning (2025.findings-emnlp)
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| Challenge: | Current Chain-of-Thought based ESC methods often employ rigid, text-only reasoning, limiting adaptability in dynamic, multimodal interactions and introducing reasoning noise that degrades support quality. |
| Approach: | They propose a framework that integrates supervised fine-tuning with reinforcement learning to improve ESC models' response quality. |
| Outcome: | The proposed framework enables models to select contextually relevant thinking aspects: Visual Scene, Emotion, Situation, and Response Strategy. |
EmoDynamiX: Emotional Support Dialogue Strategy Prediction by Modelling MiXed Emotions and Discourse Dynamics (2025.naacl-long)
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| Challenge: | Recent studies show that implicit strategy planning lacks transparency and that LLMs’ inherent preference bias towards certain socio-emotional strategies hinders the delivery of high-quality emotional support. |
| Approach: | They propose to decouple strategy prediction from language generation and introduce a new dialogue strategy prediction framework, EmoDynamiX, which models the discourse dynamics between user fine-grained emotions and system strategies using a heterogeneous graph for better performance and transparency. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on two ESC datasets with a significant margin (better proficiency and lower preference bias). |
Beyond Coarse Labels: Fine-Grained Problem Augmentation and Multi-Dimensional Feedback for Emotional Support Conversation (2025.findings-emnlp)
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| Challenge: | Existing ESC datasets often use coarse-grained problem categories, limiting models’ ability to address users’ complex, overlapping challenges. |
| Approach: | They propose a generalizable fine-grained problem enhancement method that augments problem types, user scenarios, and profiles, enabling the construction of richer and more diverse ESC corpora. |
| Outcome: | The proposed method improves both automatic and human evaluation metrics across different models. |
Facilitating Multi-turn Emotional Support Conversation with Positive Emotion Elicitation: A Reinforcement Learning Approach (2023.acl-long)
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| Challenge: | Existing approaches to provide emotional support (ESC) ignore the effect on ES and lack explicit goals to guide emotional positive transition. |
| Approach: | They propose a new paradigm to formalize multi-turn ESC as a process of positive emotion elicitation. |
| Outcome: | The proposed model outperforms existing models in achieving positive emotion elicitation while maintaining conversational goals like coherence. |
AugESC: Dialogue Augmentation with Large Language Models for Emotional Support Conversation (2023.findings-acl)
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| Challenge: | Crowdsourced dialogue corpora are limited in scale and topic coverage due to the expensive cost of data curation. |
| Approach: | They construct an augmented dataset for the emotional support conversation task using large language models for dialogue augmentation. |
| Outcome: | The proposed approach outperforms baselines of dialogue augmentation and improves the model's generalization ability to open-domain topics. |
Convert Language Model into a Value-based Strategic Planner (2025.acl-industry)
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| Challenge: | Emotional support conversation (ESC) aims to alleviate the emotional distress of individuals through effective conversations. |
| Approach: | They propose a framework that bootstraps the planning during ESC and determines the optimal strategy based on long-term returns. |
| Outcome: | The proposed framework outperforms baseline models on ESC datasets and can be used to guide the LLM to response. |
A Review of Dataset and Labeling Methods for Causality Extraction (2020.coling-main)
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| Challenge: | Existing methods for causal relationship extraction are limited and lack of unified methods hinder progress in the field. |
| Approach: | They propose to summarize existing methods and propose a new causal sequence label method . they propose to use multiple candidate causal label sequences according to label controversy . |
| Outcome: | The proposed method summarises existing methods and explores their practicability and extensibility from multiple perspectives. |
Knowledge-enhanced Mixed-initiative Dialogue System for Emotional Support Conversations (2023.acl-long)
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| Challenge: | Experimental results show the superiority of a mixed-initiative framework for emotional support conversation (ESC) ESC systems are emerging to provide prompt and convenient emotional support for helpseekers, including mental health support, counseling or motivational interviewing. |
| Approach: | They propose a knowledge-enhanced mixed-initiative framework that retrieves actual case knowledge from a large-scale mental health knowledge graph for generating mixed-initiative responses. |
| Outcome: | The proposed framework retrieves actual case knowledge from a large-scale mental health knowledge graph for generating mixed-initiative responses. |
MultiAgentESC: A LLM-based Multi-Agent Collaboration Framework for Emotional Support Conversation (2025.emnlp-main)
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| Challenge: | Existing studies focus on generating responses directly and neglect integration of domain-specific reasoning and expert interaction. |
| Approach: | They propose a training-free multi-agent collaboration framework for ESC to emulate human-like process of providing emotional support through dialogue analysis, strategy deliberation, and response generation. |
| Outcome: | The proposed framework excels at providing emotional support and diversifying support strategy selection. |
Towards Emotional Support Dialog Systems (2021.acl-long)
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| Challenge: | Emotional support is a crucial ability for many conversation scenarios, including social interactions, mental health support, and customer service chats. |
| Approach: | They propose an Emotional Support Conversation task and an ESC Framework to train emotional support into dialog systems. |
| Outcome: | The proposed framework provides an example of an Emotional Support Conversation task and shows that it is more effective than existing models. |
From Personas to Talks: Revisiting the Impact of Personas on LLM-Synthesized Emotional Support Conversations (2025.emnlp-main)
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| Challenge: | Experimental results show that LLMs can infer persona traits and subtle shifts in emotionality and extraversion occur . scalable solutions with reduced costs and enhanced data privacy are needed . |
| Approach: | They explore the role of personas in the creation of emotional support conversations by LLMs. |
| Outcome: | The proposed model can infer persona traits and maintain key persona characteristics while revealing shifts in emotionality and extraversion. |
ESC: Redesigning WSD with Extractive Sense Comprehension (2021.naacl-main)
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| Challenge: | Word Sense Disambiguation (WSD) is a historical NLP task aimed at linking words in contexts to discrete sense inventories. |
| Approach: | They propose a transformer-based neural architecture for extractive Sense Comprehension to solve a span extraction problem and a new state of the art English WSD task. |
| Outcome: | The proposed model outdoes all of its competitors while relying on three times fewer annotations. |
Make Every Penny Count: Difficulty-Adaptive Self-Consistency for Cost-Efficient Reasoning (2025.findings-naacl)
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Xinglin Wang, Shaoxiong Feng, Yiwei Li, Peiwen Yuan, Yueqi Zhang, Chuyi Tan, Boyuan Pan, Yao Hu, Kan Li
| Challenge: | Existing decoding strategies for chain-of-thought reasoning do not exploit prior information about question difficulty. |
| Approach: | They propose a decoding strategy called self-consistency to improve reasoning performance by adjusting the number of samples based on the posterior distribution of a set of pre-samples. |
| Outcome: | The proposed method outperforms baseline methods on arithmetic, commonsense and symbolic reasoning tasks while achieving comparable performance. |
TransESC: Smoothing Emotional Support Conversation via Turn-Level State Transition (2023.findings-acl)
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| Challenge: | Emotion Support Conversation (ESC) is a goaldirected task with the goal of reducing the emotional distress of people. |
| Approach: | They propose to take turn-level state Transitions of ESC from three perspectives to generate smooth transitions between utterances. |
| Outcome: | The proposed method generates smoother and more effective responses on automatic and human evaluations. |
ESC: Efficient Speech Coding with Cross-Scale Residual Vector Quantized Transformers (2024.emnlp-main)
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| Challenge: | Existing neural speech codecs trade model complexity for reconstruction performance . ESC is a lightweight, parameter-efficient speech coder . |
| Approach: | They propose an efficient speech codec based on a cross-scale residual vector quantization scheme and transformers that can achieve high-fidelity speech reconstruction with significantly lower model complexity. |
| Outcome: | The proposed codec achieves high-fidelity speech reconstruction with significantly lower model complexity. |
Improving the Efficiency of Grammatical Error Correction with Erroneous Span Detection and Correction (2020.emnlp-main)
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| Challenge: | Existing methods to improve the efficiency of GEC are not efficient enough for GEC. |
| Approach: | They propose a language-independent approach to improve the efficiency of GEC by dividing the task into two subtasks: ESD and ESC. |
| Outcome: | The proposed approach performs comparably to conventional seq2seq approaches in English and Chinese GEC benchmarks with less than 50% time cost for inference. |
Cognitive Policy-Driven LLM for Diagnosis and Intervention of Cognitive Distortions in Emotional Support Conversation (2026.acl-long)
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| Challenge: | Existing models for ESC ignore cognitive distortions in help-seekers' expressions . current models provide basic emotional comfort, rather than helping help- seekers address psychological distress at a deeper cognitive level. |
| Approach: | They propose a Large Language Model framework to enhance LLMs' ability to diagnose and intervene cognitive distortions in help-seekers. |
| Outcome: | The proposed framework outperforms 15 state-of-the-art baselines in terms of distortion diagnosis accuracy, intervention strategy effectiveness, and safety risk control. |
ESC-Judge: A Framework for Comparing Emotional Support Conversational Agents (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) increasingly power mental-health chatbots . yet the field lacks a scalable, theory-grounded way to decide which model is more effective to deploy. |
| Approach: | They propose a framework that grounds head-to-head comparisons of Emotional-Support LLMs in Hill’s Exploration–Insight–Action counselling model. |
| Outcome: | The proposed framework matches PhD-level annotators in 85% of Exploration, 83% of Insight, and 86% of Action decisions, demonstrating human-level reliability at a fraction of the cost. |
Chain of Strategy Optimization Makes Large Language Models Better Emotional Supporter (2025.findings-emnlp)
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Weixiang Zhao, Xingyu Sui, Xinyang Han, Yang Deng, Yulin Hu, Jiahe Guo, Libo Qin, Qianyun Du, Shijin Wang, Yanyan Zhao, Bing Qin, Ting Liu
| Challenge: | Existing supervised fine-tuning (SFT) fails to address these issues, as it trains models on single gold-standard responses without modeling nuanced strategy trade-offs. |
| Approach: | They propose a two-stage framework that optimizes strategy selection preferences at each dialogue turn. |
| Outcome: | The proposed framework improves strategy selection preferences at each dialogue turn. |
Beyond Semantic Similarity: Appraisal-Guided Chain-of-Thought Reasoning and Retrieval for Multimodal Emotional Support Conversations (2026.findings-acl)
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| Challenge: | Existing retrieval-augmented generation paradigms rely on semantic similarity to retrieve historical dialogues that are surface analogous but therapeutically incongruent. |
| Approach: | They propose to use appraisal-guided reasoning chains to generate appraisal-based reasoning chains and apply a dual-signal verification mechanism to verify and correct them. |
| Outcome: | Extensive experiments on two ESC benchmarks show that the proposed model significantly outperforms state-of-the-art models. |
ESC-Eval: Evaluating Emotion Support Conversations in Large Language Models (2024.emnlp-main)
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Haiquan Zhao, Lingyu Li, Shisong Chen, Shuqi Kong, Jiaan Wang, Kexin Huang, Tianle Gu, Yixu Wang, Jian Wang, Liang Dandan, Zhixu Li, Yan Teng, Yanghua Xiao, Yingchun Wang
| Challenge: | Emotion Support Conversation (ESC) is a crucial application for reducing stress and providing emotional guidance. |
| Approach: | They re-organize 2,801 role-playing cards to define roles of role-players . they train a specific role- playing model called ESC-Role which behaves more like a confused person than GPT-4 . |
| Outcome: | The proposed model behaves more like a confused person than GPT-4, and the model performs better than GPLs. |
Causal-ESC: Reliable Policy Learning for Emotional Support Conversation via Causal Inference (2026.acl-long)
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| Challenge: | Existing approaches to Emotional Support Conversation (ESC) are mechanistically opaque and lacks a causal mechanism between dialogue features and effective empathic strategies. |
| Approach: | They propose a framework that uses Doubly Robust learning to model causal effects of utterance features on strategy selection. |
| Outcome: | The proposed framework outperforms state-of-the-art baselines in empathy and helpfulness and provides a theoretically grounded, interpretable solution to the mechanistic interpretability dilemma in affective computing. |
Multi-stream Information Fusion Framework for Emotional Support Conversation (2024.lrec-main)
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| Challenge: | Existing methods for ESC do not capture the dynamic transition of emotion intensity due to the difficulty to model its dynamic transition. |
| Approach: | They propose to fuse three streams for the effective modelling of emotion intensity using a multi-stream fusion unit. |
| Outcome: | The proposed model reduces the emotional distress of users with high-intensity of negative emotions by incorporating three different kinds of streams for the dynamic transition of emotion intensity. |
DecoupledESC: Enhancing Emotional Support Generation via Strategy-Response Decoupled Preference Optimization (2025.findings-emnlp)
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| Challenge: | Existing ESC data entangles psychological strategies and response content, making it difficult to construct high-quality preference pairs. |
| Approach: | They propose a Decoupled ESC framework that decomposes the ESC task into two sequential subtasks: strategy planning and empathic response generation. |
| Outcome: | The proposed framework outperforms baselines, reducing preference bias and improving response quality. |
Emotion Trajectory-aware Retrieval for Markov-driven Emotion Anticipation in LLM-based Emotional Support Conversation (2026.findings-acl)
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| Challenge: | Existing strategies focus on planning the next-turn dialogue strategies, while external strategy planners focus on generating empathetic responses. |
| Approach: | They propose a Markov-driven emotion anticipation framework with emotion trajectory-aware retrieval for LLM-based ESC, which anticipates future emotion states to guide strategy planning and achieve sustained emotional support. |
| Outcome: | The proposed framework can anticipate future emotions and achieve sustained emotional support on two datasets with two models. |
TEA-Bench: A Systematic Benchmarking of Tool-enhanced Emotional Support Dialogue Agent (2026.acl-long)
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| Challenge: | Existing ESC systems focus on affective support in text-only settings, ignoring how external tools can enable factual grounding and reduce hallucination in multi-turn emotional support. |
| Approach: | They propose a benchmark for evaluating tool-augmented agents in ESC with realistic emotional scenarios and an MCP-style tool environment. |
| Outcome: | The proposed benchmarks show that tool augmentation improves emotional support quality and reduces hallucination, but weaker models benefit only marginally. |