# EmpDG: Multiresolution Interactive Empathetic Dialogue Generation

arXiv: https://arxiv.org/abs/1911.08698 · HTML: https://mlsanity.com/paper/1911.08698/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2019.

## Built on by (most cited first, 14 of 14)

- [Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning](https://mlsanity.com/paper/2210.04242/) (2022)
- [TransESC: Smoothing Emotional Support Conversation via Turn-Level State Transition](https://mlsanity.com/paper/2305.03296/) (2023)
- [Empathic Conversations: A Multi-level Dataset of Contextualized Conversations](https://mlsanity.com/paper/2205.12698/) (2022)
- [Empathetic Dialogue Generation via Sensitive Emotion Recognition and Sensible Knowledge Selection](https://mlsanity.com/paper/2210.11715/) (2022)
- [CASE: Aligning Coarse-to-Fine Cognition and Affection for Empathetic Response Generation](https://mlsanity.com/paper/2208.08845/) (2022)
- [Improving Contextual Coherence in Variational Personalized and Empathetic Dialogue Agents](https://mlsanity.com/paper/2202.05971/) (2022)
- [BiSyn-GAT+: Bi-Syntax Aware Graph Attention Network for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2204.03117/) (2022)
- [Exemplars-guided Empathetic Response Generation Controlled by the Elements of Human Communication](https://mlsanity.com/paper/2106.11791/) (2021)
- [Empathetic Dialog Generation with Fine-Grained Intents](https://mlsanity.com/paper/2105.06829/) (2021)
- [Towards Standard Criteria for human evaluation of Chatbots: A Survey](https://mlsanity.com/paper/2105.11197/) (2021)
- [CAB: Empathetic Dialogue Generation with Cognition, Affection and Behavior](https://mlsanity.com/paper/2302.01935/) (2023)
- [Constructing Emotion Consensus and Utilizing Unpaired Data for Empathetic Dialogue Generation](https://mlsanity.com/paper/2109.07779/) (2021)
- [Acoustic Modeling for End-to-End Empathetic Dialogue Speech Synthesis Using Linguistic and Prosodic Contexts of Dialogue History](https://mlsanity.com/paper/2206.08039/) (2022)
- [A Unified Framework for Emotion Identification and Generation in Dialogues](https://mlsanity.com/paper/2205.15513/) (2022)

## Abstract (arXiv)

A humanized dialogue system is expected to generate empathetic replies, which should be sensitive to the users' expressed emotion. The task of empathetic dialogue generation is proposed to address this problem. The essential challenges lie in accurately capturing the nuances of human emotion and considering the potential of user feedback, which are overlooked by the majority of existing work. In response to this problem, we propose a multi-resolution adversarial model -- EmpDG, to generate more empathetic responses. EmpDG exploits both the coarse-grained dialogue-level and fine-grained token-level emotions, the latter of which helps to better capture the nuances of user emotion. In addition, we introduce an interactive adversarial learning framework which exploits the user feedback, to identify whether the generated responses evoke emotion perceptivity in dialogues. Experimental results show that the proposed approach significantly outperforms the state-of-the-art baselines in both content quality and emotion perceptivity.