# Exemplars-guided Empathetic Response Generation Controlled by the Elements of Human Communication

arXiv: https://arxiv.org/abs/2106.11791 · HTML: https://mlsanity.com/paper/2106.11791/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 50 references that are in the map.
- First posted on arXiv 22 Jun 2021.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Self-Attention with Relative Position Representations](https://mlsanity.com/paper/1803.02155/) (2018)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [Retrieval Augmentation Reduces Hallucination in Conversation](https://mlsanity.com/paper/2104.07567/) (2021)
- [Music Transformer](https://mlsanity.com/paper/1809.04281/) (2018)
- [TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents](https://mlsanity.com/paper/1901.08149/) (2019)
- [A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support](https://mlsanity.com/paper/2009.08441/) (2020)
- [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)
- [Generating Sentences by Editing Prototypes](https://mlsanity.com/paper/1709.08878/) (2017)
- [Retrieve and Refine: Improved Sequence Generation Models For Dialogue](https://mlsanity.com/paper/1808.04776/) (2018)
- [Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach](https://mlsanity.com/paper/2101.07714/) (2021)
- [MojiTalk: Generating Emotional Responses at Scale](https://mlsanity.com/paper/1711.04090/) (2017)
- [Neural Syntactic Preordering for Controlled Paraphrase Generation](https://mlsanity.com/paper/2005.02013/) (2020)
- [Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory](https://mlsanity.com/paper/1809.05296/) (2018)
- [Syntax-guided Controlled Generation of Paraphrases](https://mlsanity.com/paper/2005.08417/) (2020)
- [Controllable Paraphrase Generation with a Syntactic Exemplar](https://mlsanity.com/paper/1906.00565/) (2019)
- [Generating Empathetic Responses by Looking Ahead the User's Sentiment](https://mlsanity.com/paper/1906.08487/) (2019)
- [EmpDG: Multiresolution Interactive Empathetic Dialogue Generation](https://mlsanity.com/paper/1911.08698/) (2019)
- [Generative Deep Neural Networks for Dialogue: A Short Review](https://mlsanity.com/paper/1611.06216/) (2016)
- [A Simple Dual-decoder Model for Generating Response with Sentiment](https://mlsanity.com/paper/1905.06597/) (2019)

## Built on by (most cited first, 9 of 9)

- [Human-AI Collaboration Enables More Empathic Conversations in Text-based Peer-to-Peer Mental Health Support](https://mlsanity.com/paper/2203.15144/) (2022)
- [Empathetic Conversational Systems: A Review of Current Advances, Gaps, and Opportunities](https://mlsanity.com/paper/2206.05017/) (2022)
- [Leveraging Large Language Models for Automated Dialogue Analysis](https://mlsanity.com/paper/2309.06490/) (2023)
- [Improving Empathetic Dialogue Generation by Dynamically Infusing Commonsense Knowledge](https://mlsanity.com/paper/2306.04657/) (2023)
- [Reasoning before Responding: Integrating Commonsense-based Causality Explanation for Empathetic Response Generation](https://mlsanity.com/paper/2308.00085/) (2023)
- [PoKE: Prior Knowledge Enhanced Emotional Support Conversation with Latent Variable](https://mlsanity.com/paper/2210.12640/) (2022)
- [Dynamic Demonstration Retrieval and Cognitive Understanding for Emotional Support Conversation](https://mlsanity.com/paper/2404.02505/) (2024)
- [StyEmp: Stylizing Empathetic Response Generation via Multi-Grained Prefix Encoder and Personality Reinforcement](https://mlsanity.com/paper/2408.02271/) (2024)
- [Following the TRACE: A Structured Path to Empathetic Response Generation with Multi-Agent Models](https://mlsanity.com/paper/2509.21849/) (2025)

## Abstract (arXiv)

The majority of existing methods for empathetic response generation rely on the emotion of the context to generate empathetic responses. However, empathy is much more than generating responses with an appropriate emotion. It also often entails subtle expressions of understanding and personal resonance with the situation of the other interlocutor. Unfortunately, such qualities are difficult to quantify and the datasets lack the relevant annotations. To address this issue, in this paper we propose an approach that relies on exemplars to cue the generative model on fine stylistic properties that signal empathy to the interlocutor. To this end, we employ dense passage retrieval to extract relevant exemplary responses from the training set. Three elements of human communication -- emotional presence, interpretation, and exploration, and sentiment are additionally introduced using synthetic labels to guide the generation towards empathy. The human evaluation is also extended by these elements of human communication. We empirically show that these approaches yield significant improvements in empathetic response quality in terms of both automated and human-evaluated metrics. The implementation is available at https://github.com/declare-lab/exemplary-empathy.