mlsanityData to 14 Oct 2026

Empathetic Response Generation through Graph-based Multi-hop Reasoning on Emotional Causality

arXiv 2110.04614 ↗First posted 9 Oct 2021cs.CLcited by 9

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Abstract (arXiv)

Empathetic response generation aims to comprehend the user emotion and then respond to it appropriately. Most existing works merely focus on what the emotion is and ignore how the emotion is evoked, thus weakening the capacity of the model to understand the emotional experience of the user for generating empathetic responses. To tackle this problem, we consider the emotional causality, namely, what feelings the user expresses (i.e., emotion) and why the user has such feelings (i.e., cause). Then, we propose a novel graph-based model with multi-hop reasoning to model the emotional causality of the empathetic conversation. Finally, we demonstrate the effectiveness of our model on EMPATHETICDIALOGUES in comparison with several competitive models.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 21 of its 46 references that are in the map. First posted on arXiv 9 Oct 2021.

Builds on

21 of 46 references.

References · 21

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  3. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,576
  4. 2018Universal Transformerscited by 647
  5. 2020A Survey on Knowledge Graphs: Representation, Acquisition and Applicationscited by 643
  6. 2016How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generationcited by 356
  7. 2019KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoningcited by 216
  8. 2018The Design and Implementation of XiaoIce, an Empathetic Social Chatbotcited by 172
  9. 2017Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memorycited by 167
  10. 2020MIME: MIMicking Emotions for Empathetic Response Generationcited by 114
  11. 2019MoEL: Mixture of Empathetic Listenerscited by 91
  12. 2019Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Textscited by 83
9 more
  1. 2017MojiTalk: Generating Emotional Responses at Scalecited by 64
  2. 2020Language Generation with Multi-Hop Reasoning on Commonsense Knowledge Graphcited by 57
  3. 2017Massive Exploration of Neural Machine Translation Architecturescited by 55
  4. 2018Commonsense for Generative Multi-Hop Question Answering Taskscited by 48
  5. 2019RTHN: A RNN-Transformer Hierarchical Network for Emotion Cause Extractioncited by 25
  6. 2017A Question Answering Approach to Emotion Cause Extractioncited by 21
  7. 2018An Affect-Rich Neural Conversational Model with Biased Attention and Weighted Cross-Entropy Losscited by 21
  8. 2018Sentiment Adaptive End-to-End Dialog Systemscited by 17
  9. 2020Graph-based Multi-hop Reasoning for Long Text Generationcited by 4

Built on by

Cited by year

Citing papers per year: 2022: 4; 2023: 1; 2024: 3; 2025: 1.

9 papers.

  1. 2022Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planningcited by 60
  2. 2022FADO: Feedback-Aware Double COntrolling Network for Emotional Support Conversationcited by 18
  3. 2022CARE: Causality Reasoning for Empathetic Responses by Conditional Graph Generationcited by 7
  4. 2023CAB: Empathetic Dialogue Generation with Cognition, Affection and Behaviorcited by 6
  5. 2024Mitigating Unhelpfulness in Emotional Support Conversations with Multifaceted AI Feedbackcited by 5
  6. 2024Narrating Causal Graphs with Large Language Modelscited by 4
  7. 2022Modeling Content-Emotion Duality via Disentanglement for Empathetic Conversationcited by 1
  8. 2025Does the Appearance of Autonomous Conversational Robots Affect User Spoken Behaviors in Real-World Conference Interactions?cited by 1
  9. 2024EMO-KNOW: A Large Scale Dataset on Emotion and Emotion-causecited by 0