# A Critical Reflection and Forward Perspective on Empathy and Natural Language Processing

arXiv: https://arxiv.org/abs/2210.16604 · HTML: https://mlsanity.com/paper/2210.16604/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 43 of its 183 references that are in the map.
- First posted on arXiv 29 Oct 2022.

## Builds on (most cited first, 30 of 43)

- [Recipes for building an open-domain chatbot](https://mlsanity.com/paper/2004.13637/) (2020)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [Abductive Commonsense Reasoning](https://mlsanity.com/paper/1908.05739/) (2019)
- [A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support](https://mlsanity.com/paper/2009.08441/) (2020)
- [The Design and Implementation of XiaoIce, an Empathetic Social Chatbot](https://mlsanity.com/paper/1812.08989/) (2018)
- [Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills](https://mlsanity.com/paper/2004.08449/) (2020)
- [MIME: MIMicking Emotions for Empathetic Response Generation](https://mlsanity.com/paper/2010.01454/) (2020)
- [MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation](https://mlsanity.com/paper/2107.06779/) (2021)
- [PsyQA: A Chinese Dataset for Generating Long Counseling Text for Mental Health Support](https://mlsanity.com/paper/2106.01702/) (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)
- [MISC: A MIxed Strategy-Aware Model Integrating COMET for Emotional Support Conversation](https://mlsanity.com/paper/2203.13560/) (2022)
- [Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach](https://mlsanity.com/paper/2101.07714/) (2021)
- [DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations](https://mlsanity.com/paper/2106.01978/) (2021)
- [MojiTalk: Generating Emotional Responses at Scale](https://mlsanity.com/paper/1711.04090/) (2017)
- [Towards Persona-Based Empathetic Conversational Models](https://mlsanity.com/paper/2004.12316/) (2020)
- [Modeling Empathy and Distress in Reaction to News Stories](https://mlsanity.com/paper/1808.10399/) (2018)
- [A Taxonomy of Empathetic Response Intents in Human Social Conversations](https://mlsanity.com/paper/2012.04080/) (2020)
- [Deconstruct to Reconstruct a Configurable Evaluation Metric for Open-Domain Dialogue Systems](https://mlsanity.com/paper/2011.00483/) (2020)
- [The Dialogue Dodecathlon: Open-Domain Knowledge and Image Grounded Conversational Agents](https://mlsanity.com/paper/1911.03768/) (2019)
- [CDL: Curriculum Dual Learning for Emotion-Controllable Response Generation](https://mlsanity.com/paper/2005.00329/) (2020)
- [Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes](https://mlsanity.com/paper/2109.08828/) (2021)
- [CoMAE: A Multi-factor Hierarchical Framework for Empathetic Response Generation](https://mlsanity.com/paper/2105.08316/) (2021)
- [Balancing Objectives in Counseling Conversations: Advancing Forwards or Looking Backwards](https://mlsanity.com/paper/2005.04245/) (2020)
- [EmpathBERT: A BERT-based Framework for Demographic-aware Empathy Prediction](https://mlsanity.com/paper/2102.00272/) (2021)
- [Quantifying the Causal Effects of Conversational Tendencies](https://mlsanity.com/paper/2009.03897/) (2020)
- [Annotating and Modeling Empathy in Spoken Conversations](https://mlsanity.com/paper/1705.04839/) (2017)
- [Learning Word Ratings for Empathy and Distress from Document-Level User Responses](https://mlsanity.com/paper/1912.01079/) (2019)
- [Transferable Persona-Grounded Dialogues via Grounded Minimal Edits](https://mlsanity.com/paper/2109.07713/) (2021)
- [Style Control for Schema-Guided Natural Language Generation](https://mlsanity.com/paper/2109.12211/) (2021)

## Built on by (most cited first, 4 of 4)

- [Computer says "No": The Case Against Empathetic Conversational AI](https://mlsanity.com/paper/2212.10983/) (2022)
- [Empathy Detection from Text, Audiovisual, Audio or Physiological Signals: A Systematic Review of Task Formulations and Machine Learning Methods](https://mlsanity.com/paper/2311.00721/) (2023)
- [Challenges of GPT-3-based Conversational Agents for Healthcare](https://mlsanity.com/paper/2308.14641/) (2023)
- [From Generation to Collaboration: Using LLMs to Edit for Empathy in Healthcare](https://mlsanity.com/paper/2601.15558/) (2026)

## Abstract (arXiv)

We review the state of research on empathy in natural language processing and identify the following issues: (1) empathy definitions are absent or abstract, which (2) leads to low construct validity and reproducibility. Moreover, (3) emotional empathy is overemphasized, skewing our focus to a narrow subset of simplified tasks. We believe these issues hinder research progress and argue that current directions will benefit from a clear conceptualization that includes operationalizing cognitive empathy components. Our main objectives are to provide insight and guidance on empathy conceptualization for NLP research objectives and to encourage researchers to pursue the overlooked opportunities in this area, highly relevant, e.g., for clinical and educational sectors.