# TransESC: Smoothing Emotional Support Conversation via Turn-Level State Transition

arXiv: https://arxiv.org/abs/2305.03296 · HTML: https://mlsanity.com/paper/2305.03296/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 33 references that are in the map.
- First posted on arXiv 5 May 2023.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Recipes for building an open-domain chatbot](https://mlsanity.com/paper/2004.13637/) (2020)
- [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://mlsanity.com/paper/1906.05317/) (2019)
- [Towards Emotional Support Dialog Systems](https://mlsanity.com/paper/2106.01144/) (2021)
- [COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs](https://mlsanity.com/paper/2010.05953/) (2020)
- [Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory](https://mlsanity.com/paper/1704.01074/) (2017)
- [MIME: MIMicking Emotions for Empathetic Response Generation](https://mlsanity.com/paper/2010.01454/) (2020)
- [MoEL: Mixture of Empathetic Listeners](https://mlsanity.com/paper/1908.07687/) (2019)
- [CEM: Commonsense-aware Empathetic Response Generation](https://mlsanity.com/paper/2109.05739/) (2021)
- [MISC: A MIxed Strategy-Aware Model Integrating COMET for Emotional Support Conversation](https://mlsanity.com/paper/2203.13560/) (2022)
- [MojiTalk: Generating Emotional Responses at Scale](https://mlsanity.com/paper/1711.04090/) (2017)
- [Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning](https://mlsanity.com/paper/2210.04242/) (2022)
- [Control Globally, Understand Locally: A Global-to-Local Hierarchical Graph Network for Emotional Support Conversation](https://mlsanity.com/paper/2204.12749/) (2022)
- [Towards Persona-Based Empathetic Conversational Models](https://mlsanity.com/paper/2004.12316/) (2020)
- [Knowledge Bridging for Empathetic Dialogue Generation](https://mlsanity.com/paper/2009.09708/) (2020)
- [CDL: Curriculum Dual Learning for Emotion-Controllable Response Generation](https://mlsanity.com/paper/2005.00329/) (2020)
- [Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances](https://mlsanity.com/paper/2106.02227/) (2021)
- [FADO: Feedback-Aware Double COntrolling Network for Emotional Support Conversation](https://mlsanity.com/paper/2211.00250/) (2022)
- [Don't Lose Yourself! Empathetic Response Generation via Explicit Self-Other Awareness](https://mlsanity.com/paper/2210.03884/) (2022)
- [EmpDG: Multiresolution Interactive Empathetic Dialogue Generation](https://mlsanity.com/paper/1911.08698/) (2019)
- [Annotating and Modeling Empathy in Spoken Conversations](https://mlsanity.com/paper/1705.04839/) (2017)
- [Knowledge-based Review Generation by Coherence Enhanced Text Planning](https://mlsanity.com/paper/2105.03815/) (2021)

## Built on by (most cited first, 30 of 30)

- [Can Large Language Models be Good Emotional Supporter? Mitigating Preference Bias on Emotional Support Conversation](https://mlsanity.com/paper/2402.13211/) (2024)
- [COOPER: Coordinating Specialized Agents towards a Complex Dialogue Goal](https://mlsanity.com/paper/2312.11792/) (2023)
- [Psychological Counseling Cannot Be Achieved Overnight: Automated Psychological Counseling Through Multi-Session Conversations](https://mlsanity.com/paper/2506.06626/) (2025)
- [IntentionESC: An Intention-Centered Framework for Enhancing Emotional Support in Dialogue Systems](https://mlsanity.com/paper/2506.05947/) (2025)
- [EmoDynamiX: Emotional Support Dialogue Strategy Prediction by Modelling MiXed Emotions and Discourse Dynamics](https://mlsanity.com/paper/2408.08782/) (2024)
- [Steering Conversational Large Language Models for Long Emotional Support Conversations](https://mlsanity.com/paper/2402.10453/) (2024)
- [ESC-Judge: A Framework for Comparing Emotional Support Conversational Agents](https://mlsanity.com/paper/2505.12531/) (2025)
- [AutoPal: Autonomous Adaptation to Users for Personal AI Companionship](https://mlsanity.com/paper/2406.13960/) (2024)
- [Mitigating Unhelpfulness in Emotional Support Conversations with Multifaceted AI Feedback](https://mlsanity.com/paper/2401.05928/) (2024)
- [Dynamic Demonstration Retrieval and Cognitive Understanding for Emotional Support Conversation](https://mlsanity.com/paper/2404.02505/) (2024)
- [Convert Language Model into a Value-based Strategic Planner](https://mlsanity.com/paper/2505.06987/) (2025)
- [Emotional Supporters often Use Multiple Strategies in a Single Turn](https://mlsanity.com/paper/2505.15316/) (2025)
- [Towards Open-Ended Emotional Support Conversations in LLMs via Reinforcement Learning with Future-Oriented Rewards](https://mlsanity.com/paper/2508.12935/) (2025)
- [CauESC: A Causal Aware Model for Emotional Support Conversation](https://mlsanity.com/paper/2401.17755/) (2024)
- [The Future of Cognitive Strategy-enhanced Persuasive Dialogue Agents: New Perspectives and Trends](https://mlsanity.com/paper/2402.04631/) (2024)
- [Emotional Support with LLM-based Empathetic Dialogue Generation](https://mlsanity.com/paper/2507.12820/) (2025)
- [EmoFSM: A Finite State Machine for Emotional Support Conversation](https://mlsanity.com/paper/2504.11837/) (2025)
- [FEEL: A Framework for Evaluating Emotional Support Capability with Large Language Models](https://mlsanity.com/paper/2403.15699/) (2024)
- [Toward Real-World Chinese Psychological Support Dialogues: CPsDD Dataset and a Co-Evolving Multi-Agent System](https://mlsanity.com/paper/2507.07509/) (2025)
- [TEA-Bench: A Systematic Benchmarking of Tool-enhanced Emotional Support Dialogue Agent](https://mlsanity.com/paper/2601.18700/) (2026)
- [Large Language Model Can Be a Foundation for Hidden Rationale-Based Retrieval](https://mlsanity.com/paper/2412.16615/) (2024)
- [Stress-Testing Emotional Support Models: Moving from Homogeneous to Diverse Help Seekers](https://mlsanity.com/paper/2601.07698/) (2026)
- [Discourse Diversity in Multi-Turn Empathic Dialogue](https://mlsanity.com/paper/2604.11742/) (2026)
- [ENPMR-Bench: Benchmarking Proactive Memory Retrieval for Emotional Support Agents](https://mlsanity.com/paper/2605.27240/) (2026)
- [ESC-Skills: Discovering and Self-Evolving Skills for Emotional Support Conversations](https://mlsanity.com/paper/2605.27908/) (2026)
- [Mitigating Strategy Preference Bias in Emotional Support Conversation via Uncertainty Estimations](https://mlsanity.com/paper/2509.12661/) (2025)
- [PRCCF: A Persona-guided Retrieval and Causal-aware Cognitive Filtering Framework for Emotional Support Conversation](https://mlsanity.com/paper/2604.01671/) (2026)
- [Modeling Multiple Support Strategies within a Single Turn for Emotional Support Conversations](https://mlsanity.com/paper/2604.17972/) (2026)
- [MindTailor: Personalized Emotional Support via Post History-Grounded Case Formulation and Collaborative Refinement](https://mlsanity.com/paper/2606.21930/) (2026)
- [From Triggers to Emotions: A CPM-Grounded Appraisal Multi-Agent for Dynamic Emotional Evolution in Persona-Based Dialogue](https://mlsanity.com/paper/2607.07824/) (2026)

## Abstract (arXiv)

Emotion Support Conversation (ESC) is an emerging and challenging task with the goal of reducing the emotional distress of people. Previous attempts fail to maintain smooth transitions between utterances in ESC because they ignore to grasp the fine-grained transition information at each dialogue turn. To solve this problem, we propose to take into account turn-level state Transitions of ESC (TransESC) from three perspectives, including semantics transition, strategy transition and emotion transition, to drive the conversation in a smooth and natural way. Specifically, we construct the state transition graph with a two-step way, named transit-then-interact, to grasp such three types of turn-level transition information. Finally, they are injected into the transition-aware decoder to generate more engaging responses. Both automatic and human evaluations on the benchmark dataset demonstrate the superiority of TransESC to generate more smooth and effective supportive responses. Our source code is available at urlhttps://github.com/circle-hit/TransESC.