# Empathetic Dialogue Generation via Sensitive Emotion Recognition and Sensible Knowledge Selection

arXiv: https://arxiv.org/abs/2210.11715 · HTML: https://mlsanity.com/paper/2210.11715/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 37 references that are in the map.
- First posted on arXiv 21 Oct 2022.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://mlsanity.com/paper/1906.05317/) (2019)
- [How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation](https://mlsanity.com/paper/1603.08023/) (2016)
- [Towards Emotional Support Dialog Systems](https://mlsanity.com/paper/2106.01144/) (2021)
- [A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support](https://mlsanity.com/paper/2009.08441/) (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)
- [Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach](https://mlsanity.com/paper/2101.07714/) (2021)
- [A Taxonomy of Empathetic Response Intents in Human Social Conversations](https://mlsanity.com/paper/2012.04080/) (2020)
- [CDL: Curriculum Dual Learning for Emotion-Controllable Response Generation](https://mlsanity.com/paper/2005.00329/) (2020)
- [Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes](https://mlsanity.com/paper/2109.08828/) (2021)
- [CoMAE: A Multi-factor Hierarchical Framework for Empathetic Response Generation](https://mlsanity.com/paper/2105.08316/) (2021)
- [Improving Neural Response Diversity with Frequency-Aware Cross-Entropy Loss](https://mlsanity.com/paper/1902.09191/) (2019)
- [An Affect-Rich Neural Conversational Model with Biased Attention and Weighted Cross-Entropy Loss](https://mlsanity.com/paper/1811.07078/) (2018)
- [CARE: Commonsense-Aware Emotional Response Generation with Latent Concepts](https://mlsanity.com/paper/2012.08377/) (2020)
- [EmpDG: Multiresolution Interactive Empathetic Dialogue Generation](https://mlsanity.com/paper/1911.08698/) (2019)
- [Emp-RFT: Empathetic Response Generation via Recognizing Feature Transitions between Utterances](https://mlsanity.com/paper/2205.03112/) (2022)

## Built on by (most cited first, 19 of 19)

- [Is ChatGPT Equipped with Emotional Dialogue Capabilities?](https://mlsanity.com/paper/2304.09582/) (2023)
- [Harnessing the Power of Large Language Models for Empathetic Response Generation: Empirical Investigations and Improvements](https://mlsanity.com/paper/2310.05140/) (2023)
- [Sotopia-RL: Reward Design for Social Intelligence](https://mlsanity.com/paper/2508.03905/) (2025)
- [Reasoning before Responding: Integrating Commonsense-based Causality Explanation for Empathetic Response Generation](https://mlsanity.com/paper/2308.00085/) (2023)
- [Sibyl: Empowering Empathetic Dialogue Generation in Large Language Models via Sensible and Visionary Commonsense Inference](https://mlsanity.com/paper/2311.15316/) (2023)
- [An Iterative Associative Memory Model for Empathetic Response Generation](https://mlsanity.com/paper/2402.17959/) (2024)
- [Empathy Level Alignment via Reinforcement Learning for Empathetic Response Generation](https://mlsanity.com/paper/2408.02976/) (2024)
- [Rational Sensibility: LLM Enhanced Empathetic Response Generation Guided by Self-presentation Theory](https://mlsanity.com/paper/2312.08702/) (2023)
- [Orthogonal Finetuning for Direct Preference Optimization](https://mlsanity.com/paper/2409.14836/) (2024)
- [Multi-level Adaptive Contrastive Learning for Knowledge Internalization in Dialogue Generation](https://mlsanity.com/paper/2310.08943/) (2023)
- [Dealing with Controversy: An Emotion and Coping Strategy Corpus Based on Role Playing](https://mlsanity.com/paper/2409.19025/) (2024)
- [When LLMs Team Up: The Emergence of Collaborative Affective Computing](https://mlsanity.com/paper/2506.01698/) (2025)
- [Breaking the Trade-Off Between Faithfulness and Expressiveness for Large Language Models](https://mlsanity.com/paper/2508.18651/) (2025)
- [A Factuality and Diversity Reconciled Decoding Method for Knowledge-Grounded Dialogue Generation](https://mlsanity.com/paper/2407.05718/) (2024)
- [Bridging Cognition and Emotion: Empathy-Driven Multimodal Misinformation Detection](https://mlsanity.com/paper/2504.17332/) (2025)
- [Addressing the Ecological Fallacy in Larger LMs with Human Context](https://mlsanity.com/paper/2603.05928/) (2026)
- [PRIDE: Privileged Information-enhanced Distillation for Empathetic Dialogue Generation](https://mlsanity.com/paper/2606.23124/) (2026)
- [Dynamic Commonsense Coordination for Empathetic Response Generation](https://mlsanity.com/paper/2607.22136/) (2026)
- [Towards High-Level Semantic Intelligence](https://mlsanity.com/paper/2607.24082/) (2026)

## Abstract (arXiv)

Empathy, which is widely used in psychological counselling, is a key trait of everyday human conversations. Equipped with commonsense knowledge, current approaches to empathetic response generation focus on capturing implicit emotion within dialogue context, where the emotions are treated as a static variable throughout the conversations. However, emotions change dynamically between utterances, which makes previous works difficult to perceive the emotion flow and predict the correct emotion of the target response, leading to inappropriate response. Furthermore, simply importing commonsense knowledge without harmonization may trigger the conflicts between knowledge and emotion, which confuse the model to choose incorrect information to guide the generation process. To address the above problems, we propose a Serial Encoding and Emotion-Knowledge interaction (SEEK) method for empathetic dialogue generation. We use a fine-grained encoding strategy which is more sensitive to the emotion dynamics (emotion flow) in the conversations to predict the emotion-intent characteristic of response. Besides, we design a novel framework to model the interaction between knowledge and emotion to generate more sensible response. Extensive experiments on EmpatheticDialogues demonstrate that SEEK outperforms the strong baselines in both automatic and manual evaluations.