# Facilitating Multi-turn Emotional Support Conversation with Positive Emotion Elicitation: A Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2307.07994 · HTML: https://mlsanity.com/paper/2307.07994/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 49 references that are in the map.
- First posted on arXiv 16 Jul 2023.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [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)
- [Challenges in Building Intelligent Open-domain Dialog Systems](https://mlsanity.com/paper/1905.05709/) (2019)
- [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)
- [Target-Guided Open-Domain Conversation](https://mlsanity.com/paper/1905.11553/) (2019)
- [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)
- [Knowledge Bridging for Empathetic Dialogue Generation](https://mlsanity.com/paper/2009.09708/) (2020)
- [CoMAE: A Multi-factor Hierarchical Framework for Empathetic Response Generation](https://mlsanity.com/paper/2105.08316/) (2021)
- [CASE: Aligning Coarse-to-Fine Cognition and Affection for Empathetic Response Generation](https://mlsanity.com/paper/2208.08845/) (2022)
- [Emotion Eliciting Machine: Emotion Eliciting Conversation Generation based on Dual Generator](https://mlsanity.com/paper/2105.08251/) (2021)
- [Towards Multi-Turn Empathetic Dialogs with Positive Emotion Elicitation](https://mlsanity.com/paper/2204.10509/) (2022)

## Built on by (most cited first, 15 of 15)

- [On Memory Construction and Retrieval for Personalized Conversational Agents](https://mlsanity.com/paper/2502.05589/) (2025)
- [Recursively Summarizing Enables Long-Term Dialogue Memory in Large Language Models](https://mlsanity.com/paper/2308.15022/) (2023)
- [CharacterGLM: Customizing Chinese Conversational AI Characters with Large Language Models](https://mlsanity.com/paper/2311.16832/) (2023)
- [Sentient Agent as a Judge: Evaluating Higher-Order Social Cognition in Large Language Models](https://mlsanity.com/paper/2505.02847/) (2025)
- [Mixture of Experts (MoE): A Big Data Perspective](https://mlsanity.com/paper/2501.16352/) (2025)
- [Large Language Models are Capable of Offering Cognitive Reappraisal, if Guided](https://mlsanity.com/paper/2404.01288/) (2024)
- [ES-MemEval: Benchmarking Conversational Agents on Personalized Long-Term Emotional Support](https://mlsanity.com/paper/2602.01885/) (2026)
- [Emotional Support with LLM-based Empathetic Dialogue Generation](https://mlsanity.com/paper/2507.12820/) (2025)
- [Persona Extraction Through Semantic Similarity for Emotional Support Conversation Generation](https://mlsanity.com/paper/2403.04212/) (2024)
- [Affective Flow Language Model for Emotional Support Conversation](https://mlsanity.com/paper/2602.08826/) (2026)
- [MICA: Multi-granularity Intertemporal Credit Assignment for Long-Horizon Emotional Support Dialogue](https://mlsanity.com/paper/2603.06194/) (2026)
- [Discourse Diversity in Multi-Turn Empathic Dialogue](https://mlsanity.com/paper/2604.11742/) (2026)
- [Mind2: Mind-to-Mind Emotional Support System with Bidirectional Cognitive Discourse Analysis](https://mlsanity.com/paper/2503.16523/) (2025)
- [Towards Privacy-Preserving Mental Health Support with Large Language Models](https://mlsanity.com/paper/2601.01993/) (2026)
- [PRCCF: A Persona-guided Retrieval and Causal-aware Cognitive Filtering Framework for Emotional Support Conversation](https://mlsanity.com/paper/2604.01671/) (2026)

## Abstract (arXiv)

Emotional support conversation (ESC) aims to provide emotional support (ES) to improve one's mental state. Existing works stay at fitting grounded responses and responding strategies (e.g., question), which ignore the effect on ES and lack explicit goals to guide emotional positive transition. To this end, we introduce a new paradigm to formalize multi-turn ESC as a process of positive emotion elicitation. Addressing this task requires finely adjusting the elicitation intensity in ES as the conversation progresses while maintaining conversational goals like coherence. In this paper, we propose Supporter, a mixture-of-expert-based reinforcement learning model, and well design ES and dialogue coherence rewards to guide policy's learning for responding. Experiments verify the superiority of Supporter in achieving positive emotion elicitation during responding while maintaining conversational goals including coherence.