mlsanityData to 14 Oct 2026

CASE: Aligning Coarse-to-Fine Cognition and Affection for Empathetic Response Generation

arXiv 2208.08845 ↗First posted 18 Aug 2022cs.AI cs.CLcited by 18

Not on the map. Why?

Abstract (arXiv)

Empathetic conversation is psychologically supposed to be the result of conscious alignment and interaction between the cognition and affection of empathy. However, existing empathetic dialogue models usually consider only the affective aspect or treat cognition and affection in isolation, which limits the capability of empathetic response generation. In this work, we propose the CASE model for empathetic dialogue generation. It first builds upon a commonsense cognition graph and an emotional concept graph and then aligns the user's cognition and affection at both the coarse-grained and fine-grained levels. Through automatic and manual evaluation, we demonstrate that CASE outperforms state-of-the-art baselines of empathetic dialogues and can generate more empathetic and informative responses.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 30 of its 52 references that are in the map. First posted on arXiv 18 Aug 2022.

Builds on

30 of 52 references.

References · 30

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  4. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  5. 2018Learning deep representations by mutual information estimation and maximizationcited by 1,273
  6. 2015A Diversity-Promoting Objective Function for Neural Conversation Modelscited by 1,077
  7. 2016ConceptNet 5.5: An Open Multilingual Graph of General Knowledgecited by 1,067
  8. 2020Recipes for building an open-domain chatbotcited by 593
  9. 2019COMET: Commonsense Transformers for Automatic Knowledge Graph Constructioncited by 450
  10. 2016How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generationcited by 356
  11. 2020S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximizationcited by 311
  12. 2021Towards Emotional Support Dialog Systemscited by 299
18 more
  1. 2017Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memorycited by 167
  2. 2019Challenges in Building Intelligent Open-domain Dialog Systemscited by 147
  3. 2020MIME: MIMicking Emotions for Empathetic Response Generationcited by 114
  4. 2019MoEL: Mixture of Empathetic Listenerscited by 91
  5. 2021CEM: Commonsense-aware Empathetic Response Generationcited by 89
  6. 2019A Mutual Information Maximization Perspective of Language Representation Learningcited by 85
  7. 2022AugESC: Dialogue Augmentation with Large Language Models for Emotional Support Conversationcited by 79
  8. 2019Grounded Conversation Generation as Guided Traverses in Commonsense Knowledge Graphscited by 59
  9. 2020Knowledge Bridging for Empathetic Dialogue Generationcited by 50
  10. 2021CoMAE: A Multi-factor Hierarchical Framework for Empathetic Response Generationcited by 42
  11. 2021Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interactioncited by 21
  12. 2020CARE: Commonsense-Aware Emotional Response Generation with Latent Conceptscited by 20
  13. 2019EmpDG: Multiresolution Interactive Empathetic Dialogue Generationcited by 14
  14. 2022Emp-RFT: Empathetic Response Generation via Recognizing Feature Transitions between Utterancescited by 11
  15. 2021Knowledge-based Review Generation by Coherence Enhanced Text Planningcited by 10
  16. 2022EmpHi: Generating Empathetic Responses with Human-like Intentscited by 9
  17. 2021CASS: Towards Building a Social-Support Chatbot for Online Health Communitycited by 8
  18. 2022Modeling Intention, Emotion and External World in Dialogue Systemscited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 6; 2025: 5; 2026: 3.

18 papers.

Sort:

By area:

  1. 2023ChatCounselor: A Large Language Models for Mental Health Supportcited by 44
  2. 2023Harnessing the Power of Large Language Models for Empathetic Response Generation: Empirical Investigations and Improvementscited by 42
  3. 2025Sentient Agent as a Judge: Evaluating Higher-Order Social Cognition in Large Language Modelscited by 21
  4. 2023Facilitating Multi-turn Emotional Support Conversation with Positive Emotion Elicitation: A Reinforcement Learning Approachcited by 15
  5. 2024Cause-Aware Empathetic Response Generation via Chain-of-Thought Fine-Tuningcited by 11
  6. 2025Crisp: Cognitive Restructuring of Negative Thoughts through Multi-turn Supportive Dialoguescited by 11
  7. 2023Sibyl: Empowering Empathetic Dialogue Generation in Large Language Models via Sensible and Visionary Commonsense Inferencecited by 9
  8. 2024An Iterative Associative Memory Model for Empathetic Response Generationcited by 8
  9. 2024Empathy Level Alignment via Reinforcement Learning for Empathetic Response Generationcited by 8
  10. 2024ReflectDiffu:Reflect between Emotion-intent Contagion and Mimicry for Empathetic Response Generation via a RL-Diffusion Frameworkcited by 6
  11. 2024TOOL-ED: Enhancing Empathetic Response Generation with the Tool Calling Capability of LLMcited by 5
  12. 2024The Future of Cognitive Strategy-enhanced Persuasive Dialogue Agents: New Perspectives and Trendscited by 4
6 more
  1. 2025When LLMs Team Up: The Emergence of Collaborative Affective Computingcited by 2
  2. 2025Mind2: Mind-to-Mind Emotional Support System with Bidirectional Cognitive Discourse Analysiscited by 0
  3. 2025Following the TRACE: A Structured Path to Empathetic Response Generation with Multi-Agent Modelscited by 0
  4. 2026PRIDE: Privileged Information-enhanced Distillation for Empathetic Dialogue Generationcited by 0
  5. 2026Dynamic Commonsense Coordination for Empathetic Response Generationcited by 0
  6. 2026Towards High-Level Semantic Intelligencecited by 0