# CAB: Empathetic Dialogue Generation with Cognition, Affection and Behavior

arXiv: https://arxiv.org/abs/2302.01935 · HTML: https://mlsanity.com/paper/2302.01935/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 36 references that are in the map.
- First posted on arXiv 3 Feb 2023.

## Builds on (most cited first, 24 of 24)

- [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)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Generating Sentences from a Continuous Space](https://mlsanity.com/paper/1511.06349/) (2015)
- [Towards Emotional Support Dialog Systems](https://mlsanity.com/paper/2106.01144/) (2021)
- [Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders](https://mlsanity.com/paper/1703.10960/) (2017)
- [A Neural Conversational Model](https://mlsanity.com/paper/1506.05869/) (2015)
- [DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation](https://mlsanity.com/paper/1908.11540/) (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)
- [EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa](https://mlsanity.com/paper/2108.12009/) (2021)
- [A Taxonomy of Empathetic Response Intents in Human Social Conversations](https://mlsanity.com/paper/2012.04080/) (2020)
- [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)
- [Variational Transformers for Diverse Response Generation](https://mlsanity.com/paper/2003.12738/) (2020)
- [EmpDG: Multiresolution Interactive Empathetic Dialogue Generation](https://mlsanity.com/paper/1911.08698/) (2019)
- [Emotion-Regularized Conditional Variational Autoencoder for Emotional Response Generation](https://mlsanity.com/paper/2104.08857/) (2021)
- [Empathetic Response Generation through Graph-based Multi-hop Reasoning on Emotional Causality](https://mlsanity.com/paper/2110.04614/) (2021)
- [EmpHi: Generating Empathetic Responses with Human-like Intents](https://mlsanity.com/paper/2204.12191/) (2022)
- [CASS: Towards Building a Social-Support Chatbot for Online Health Community](https://mlsanity.com/paper/2101.01583/) (2021)
- [Empathetic Dialogue Generation with Pre-trained RoBERTa-GPT2 and External Knowledge](https://mlsanity.com/paper/2109.03004/) (2021)

## Built on by (most cited first, 6 of 6)

- [Harnessing Large Language Models' Empathetic Response Generation Capabilities for Online Mental Health Counselling Support](https://mlsanity.com/paper/2310.08017/) (2023)
- [Rethinking the Alignment of Psychotherapy Dialogue Generation with Motivational Interviewing Strategies](https://mlsanity.com/paper/2408.06527/) (2024)
- [Script-Strategy Aligned Generation: Aligning LLMs with Expert-Crafted Dialogue Scripts and Therapeutic Strategies for Psychotherapy](https://mlsanity.com/paper/2411.06723/) (2024)
- [ReflectDiffu:Reflect between Emotion-intent Contagion and Mimicry for Empathetic Response Generation via a RL-Diffusion Framework](https://mlsanity.com/paper/2409.10289/) (2024)
- [Towards a Multidimensional Evaluation Framework for Empathetic Conversational Systems](https://mlsanity.com/paper/2407.18538/) (2024)
- [Towards High-Level Semantic Intelligence](https://mlsanity.com/paper/2607.24082/) (2026)

## Abstract (arXiv)

Empathy is an important characteristic to be considered when building a more intelligent and humanized dialogue agent. However, existing methods did not fully comprehend empathy as a complex process involving three aspects: cognition, affection and behavior. In this paper, we propose CAB, a novel framework that takes a comprehensive perspective of cognition, affection and behavior to generate empathetic responses. For cognition, we build paths between critical keywords in the dialogue by leveraging external knowledge. This is because keywords in a dialogue are the core of sentences. Building the logic relationship between keywords, which is overlooked by the majority of existing works, can improve the understanding of keywords and contextual logic, thus enhance the cognitive ability. For affection, we capture the emotional dependencies with dual latent variables that contain both interlocutors' emotions. The reason is that considering both interlocutors' emotions simultaneously helps to learn the emotional dependencies. For behavior, we use appropriate dialogue acts to guide the dialogue generation to enhance the empathy expression. Extensive experiments demonstrate that our multi-perspective model outperforms the state-of-the-art models in both automatic and manual evaluation.