# FADO: Feedback-Aware Double COntrolling Network for Emotional Support Conversation

arXiv: https://arxiv.org/abs/2211.00250 · HTML: https://mlsanity.com/paper/2211.00250/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 49 references that are in the map.
- First posted on arXiv 1 Nov 2022.

## Builds on (most cited first, 22 of 22)

- [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)
- [Key-Value Memory Networks for Directly Reading Documents](https://mlsanity.com/paper/1606.03126/) (2016)
- [Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models](https://mlsanity.com/paper/1507.04808/) (2015)
- [The Design and Implementation of XiaoIce, an Empathetic Social Chatbot](https://mlsanity.com/paper/1812.08989/) (2018)
- [Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory](https://mlsanity.com/paper/1704.01074/) (2017)
- [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)
- [MISC: A MIxed Strategy-Aware Model Integrating COMET for Emotional Support Conversation](https://mlsanity.com/paper/2203.13560/) (2022)
- [EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa](https://mlsanity.com/paper/2108.12009/) (2021)
- [Control Globally, Understand Locally: A Global-to-Local Hierarchical Graph Network for Emotional Support Conversation](https://mlsanity.com/paper/2204.12749/) (2022)
- [CoMAE: A Multi-factor Hierarchical Framework for Empathetic Response Generation](https://mlsanity.com/paper/2105.08316/) (2021)
- [Hierarchical Recurrent Attention Network for Response Generation](https://mlsanity.com/paper/1701.07149/) (2017)
- [Don't Lose Yourself! Empathetic Response Generation via Explicit Self-Other Awareness](https://mlsanity.com/paper/2210.03884/) (2022)
- [Generating Multiple Diverse Responses for Short-Text Conversation](https://mlsanity.com/paper/1811.05696/) (2018)
- [GoG: Relation-aware Graph-over-Graph Network for Visual Dialog](https://mlsanity.com/paper/2109.08475/) (2021)
- [Empathetic Response Generation through Graph-based Multi-hop Reasoning on Emotional Causality](https://mlsanity.com/paper/2110.04614/) (2021)
- [Chat-Capsule: A Hierarchical Capsule for Dialog-level Emotion Analysis](https://mlsanity.com/paper/2203.12254/) (2022)

## Built on by (most cited first, 18 of 18)

- [SoulChat: Improving LLMs' Empathy, Listening, and Comfort Abilities through Fine-tuning with Multi-turn Empathy Conversations](https://mlsanity.com/paper/2311.00273/) (2023)
- [CharacterChat: Learning towards Conversational AI with Personalized Social Support](https://mlsanity.com/paper/2308.10278/) (2023)
- [TransESC: Smoothing Emotional Support Conversation via Turn-Level State Transition](https://mlsanity.com/paper/2305.03296/) (2023)
- [Think Twice: A Human-like Two-stage Conversational Agent for Emotional Response Generation](https://mlsanity.com/paper/2301.04907/) (2023)
- [A Cognitive Stimulation Dialogue System with Multi-source Knowledge Fusion for Elders with Cognitive Impairment](https://mlsanity.com/paper/2305.08200/) (2023)
- [Steering Conversational Large Language Models for Long Emotional Support Conversations](https://mlsanity.com/paper/2402.10453/) (2024)
- [Knowledge-enhanced Memory Model for Emotional Support Conversation](https://mlsanity.com/paper/2310.07700/) (2023)
- [Dynamic Demonstration Retrieval and Cognitive Understanding for Emotional Support Conversation](https://mlsanity.com/paper/2404.02505/) (2024)
- [The Future of Cognitive Strategy-enhanced Persuasive Dialogue Agents: New Perspectives and Trends](https://mlsanity.com/paper/2402.04631/) (2024)
- [Dynamic Strategy Chain: Dynamic Zero-Shot CoT for Long Mental Health Support Generation](https://mlsanity.com/paper/2308.10444/) (2023)
- [Psy-Insight: Explainable Multi-turn Bilingual Dataset for Mental Health Counseling](https://mlsanity.com/paper/2503.03607/) (2025)
- [MLlm-DR: Towards Explainable Depression Recognition with MultiModal Large Language Models](https://mlsanity.com/paper/2507.05591/) (2025)
- [FEEL: A Framework for Evaluating Emotional Support Capability with Large Language Models](https://mlsanity.com/paper/2403.15699/) (2024)
- [CTISum: A New Benchmark Dataset For Cyber Threat Intelligence Summarization](https://mlsanity.com/paper/2408.06576/) (2024)
- [Mind2: Mind-to-Mind Emotional Support System with Bidirectional Cognitive Discourse Analysis](https://mlsanity.com/paper/2503.16523/) (2025)
- [PsycholexTherapy: Simulating Reasoning in Psychotherapy with Small Language Models in Persian](https://mlsanity.com/paper/2510.03913/) (2025)
- [PRCCF: A Persona-guided Retrieval and Causal-aware Cognitive Filtering Framework for Emotional Support Conversation](https://mlsanity.com/paper/2604.01671/) (2026)
- [MindTailor: Personalized Emotional Support via Post History-Grounded Case Formulation and Collaborative Refinement](https://mlsanity.com/paper/2606.21930/) (2026)

## Abstract (arXiv)

Emotional Support Conversation (ESConv) aims to reduce help-seekers'emotional distress with the supportive strategy and response. It is essential for the supporter to select an appropriate strategy with the feedback of the help-seeker (e.g., emotion change during dialog turns, etc) in ESConv. However, previous methods mainly focus on the dialog history to select the strategy and ignore the help-seeker's feedback, leading to the wrong and user-irrelevant strategy prediction. In addition, these approaches only model the context-to-strategy flow and pay less attention to the strategy-to-context flow that can focus on the strategy-related context for generating the strategy-constrain response. In this paper, we propose a Feedback-Aware Double COntrolling Network (FADO) to make a strategy schedule and generate the supportive response. The core module in FADO consists of a dual-level feedback strategy selector and a double control reader. Specifically, the dual-level feedback strategy selector leverages the turn-level and conversation-level feedback to encourage or penalize strategies. The double control reader constructs the novel strategy-to-context flow for generating the strategy-constrain response. Furthermore, a strategy dictionary is designed to enrich the semantic information of the strategy and improve the quality of strategy-constrain response. Experimental results on ESConv show that the proposed FADO has achieved the state-of-the-art performance in terms of both strategy selection and response generation. Our code is available at https://github.com/Thedatababbler/FADO.