# Empathetic Response Generation via Emotion Cause Transition Graph

arXiv: https://arxiv.org/abs/2302.11787 · HTML: https://mlsanity.com/paper/2302.11787/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 32 references that are in the map.
- First posted on arXiv 23 Feb 2023.

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

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [Recipes for building an open-domain chatbot](https://mlsanity.com/paper/2004.13637/) (2020)
- [The Design and Implementation of XiaoIce, an Empathetic Social Chatbot](https://mlsanity.com/paper/1812.08989/) (2018)
- [Insertion Transformer: Flexible Sequence Generation via Insertion Operations](https://mlsanity.com/paper/1902.03249/) (2019)
- [MIME: MIMicking Emotions for Empathetic Response Generation](https://mlsanity.com/paper/2010.01454/) (2020)
- [GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-Supervised Learning and Explicit Policy Injection](https://mlsanity.com/paper/2111.14592/) (2021)
- [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)
- [Target-Guided Open-Domain Conversation](https://mlsanity.com/paper/1905.11553/) (2019)
- [UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition](https://mlsanity.com/paper/2211.11256/) (2022)
- [Grounded Conversation Generation as Guided Traverses in Commonsense Knowledge Graphs](https://mlsanity.com/paper/1911.02707/) (2019)
- [Knowledge Bridging for Empathetic Dialogue Generation](https://mlsanity.com/paper/2009.09708/) (2020)
- [Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes](https://mlsanity.com/paper/2109.08828/) (2021)
- [Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialog State Tracking](https://mlsanity.com/paper/2106.00291/) (2021)
- [SPACE-3: Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation](https://mlsanity.com/paper/2209.06664/) (2022)
- [Duplex Conversation: Towards Human-like Interaction in Spoken Dialogue Systems](https://mlsanity.com/paper/2205.15060/) (2022)
- [SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understanding](https://mlsanity.com/paper/2209.06638/) (2022)
- [Thinking Clearly, Talking Fast: Concept-Guided Non-Autoregressive Generation for Open-Domain Dialogue Systems](https://mlsanity.com/paper/2109.04084/) (2021)
- [A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots](https://mlsanity.com/paper/2203.10759/) (2022)
- [CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation](https://mlsanity.com/paper/2211.11617/) (2022)

## Built on by (most cited first, 8 of 8)

- [Harnessing the Power of Large Language Models for Empathetic Response Generation: Empirical Investigations and Improvements](https://mlsanity.com/paper/2310.05140/) (2023)
- [UniSA: Unified Generative Framework for Sentiment Analysis](https://mlsanity.com/paper/2309.01339/) (2023)
- [Speech-Text Dialog Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment](https://mlsanity.com/paper/2305.11579/) (2023)
- [Unsupervised Dialogue Topic Segmentation with Topic-aware Utterance Representation](https://mlsanity.com/paper/2305.02747/) (2023)
- [UniMEEC: Towards Unified Multimodal Emotion Recognition and Emotion Cause](https://mlsanity.com/paper/2404.00403/) (2024)
- [Self-Explanation Prompting Improves Dialogue Understanding in Large Language Models](https://mlsanity.com/paper/2309.12940/) (2023)
- [EMO-KNOW: A Large Scale Dataset on Emotion and Emotion-cause](https://mlsanity.com/paper/2406.12389/) (2024)
- [Towards High-Level Semantic Intelligence](https://mlsanity.com/paper/2607.24082/) (2026)

## Abstract (arXiv)

Empathetic dialogue is a human-like behavior that requires the perception of both affective factors (e.g., emotion status) and cognitive factors (e.g., cause of the emotion). Besides concerning emotion status in early work, the latest approaches study emotion causes in empathetic dialogue. These approaches focus on understanding and duplicating emotion causes in the context to show empathy for the speaker. However, instead of only repeating the contextual causes, the real empathic response often demonstrate a logical and emotion-centered transition from the causes in the context to those in the responses. In this work, we propose an emotion cause transition graph to explicitly model the natural transition of emotion causes between two adjacent turns in empathetic dialogue. With this graph, the concept words of the emotion causes in the next turn can be predicted and used by a specifically designed concept-aware decoder to generate the empathic response. Automatic and human experimental results on the benchmark dataset demonstrate that our method produces more empathetic, coherent, informative, and specific responses than existing models.