# Towards an Online Empathetic Chatbot with Emotion Causes

arXiv: https://arxiv.org/abs/2105.11903 · HTML: https://mlsanity.com/paper/2105.11903/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 35 references that are in the map.
- First posted on arXiv 11 May 2021.

## Builds on (most cited first, 20 of 20)

- [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)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://mlsanity.com/paper/1905.03197/) (2019)
- [TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents](https://mlsanity.com/paper/1901.08149/) (2019)
- [Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory](https://mlsanity.com/paper/1704.01074/) (2017)
- [PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable](https://mlsanity.com/paper/1910.07931/) (2019)
- [Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances](https://mlsanity.com/paper/1905.02947/) (2019)
- [MIME: MIMicking Emotions for Empathetic Response Generation](https://mlsanity.com/paper/2010.01454/) (2020)
- [Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts](https://mlsanity.com/paper/1906.01267/) (2019)
- [A Large-Scale Chinese Short-Text Conversation Dataset](https://mlsanity.com/paper/2008.03946/) (2020)
- [Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach](https://mlsanity.com/paper/2101.07714/) (2021)
- [Recognizing Emotion Cause in Conversations](https://mlsanity.com/paper/2012.11820/) (2020)
- [A Taxonomy of Empathetic Response Intents in Human Social Conversations](https://mlsanity.com/paper/2012.04080/) (2020)
- [CDL: Curriculum Dual Learning for Emotion-Controllable Response Generation](https://mlsanity.com/paper/2005.00329/) (2020)
- [A Syntactically Constrained Bidirectional-Asynchronous Approach for Emotional Conversation Generation](https://mlsanity.com/paper/1806.07000/) (2018)
- [An Empirical Investigation of Pre-Trained Transformer Language Models for Open-Domain Dialogue Generation](https://mlsanity.com/paper/2003.04195/) (2020)

## Built on by (most cited first, 16 of 16)

- [MISC: A MIxed Strategy-Aware Model Integrating COMET for Emotional Support Conversation](https://mlsanity.com/paper/2203.13560/) (2022)
- [Building Emotional Support Chatbots in the Era of LLMs](https://mlsanity.com/paper/2308.11584/) (2023)
- [Knowledge-enhanced Mixed-initiative Dialogue System for Emotional Support Conversations](https://mlsanity.com/paper/2305.10172/) (2023)
- [ChaCha: Leveraging Large Language Models to Prompt Children to Share Their Emotions about Personal Events](https://mlsanity.com/paper/2309.12244/) (2023)
- [Rethinking Large Language Models in Mental Health Applications](https://mlsanity.com/paper/2311.11267/) (2023)
- [Empathetic Conversational Systems: A Review of Current Advances, Gaps, and Opportunities](https://mlsanity.com/paper/2206.05017/) (2022)
- [BERT-ERC: Fine-tuning BERT is Enough for Emotion Recognition in Conversation](https://mlsanity.com/paper/2301.06745/) (2023)
- [C3KG: A Chinese Commonsense Conversation Knowledge Graph](https://mlsanity.com/paper/2204.02549/) (2022)
- [Wish I Can Feel What You Feel: A Neural Approach for Empathetic Response Generation](https://mlsanity.com/paper/2212.02000/) (2022)
- [Exploring Emotion-Sensitive LLM-Based Conversational AI](https://mlsanity.com/paper/2502.08920/) (2025)
- [ECQED: Emotion-Cause Quadruple Extraction in Dialogs](https://mlsanity.com/paper/2306.03969/) (2023)
- [Development and Evaluation of Three Chatbots for Postpartum Mood and Anxiety Disorders](https://mlsanity.com/paper/2308.07407/) (2023)
- ["You tell me": A Dataset of GPT-4-Based Behaviour Change Support Conversations](https://mlsanity.com/paper/2401.16167/) (2024)
- [Improving Emotional Support Delivery in Text-Based Community Safety Reporting Using Large Language Models](https://mlsanity.com/paper/2409.15706/) (2024)
- [Generative Emotion Cause Explanation in Multimodal Conversations](https://mlsanity.com/paper/2411.02430/) (2024)
- [Socio-Emotional Response Generation: A Human Evaluation Protocol for LLM-Based Conversational Systems](https://mlsanity.com/paper/2412.04492/) (2024)

## Abstract (arXiv)

Existing emotion-aware conversational models usually focus on controlling the response contents to align with a specific emotion class, whereas empathy is the ability to understand and concern the feelings and experience of others. Hence, it is critical to learn the causes that evoke the users' emotion for empathetic responding, a.k.a. emotion causes. To gather emotion causes in online environments, we leverage counseling strategies and develop an empathetic chatbot to utilize the causal emotion information. On a real-world online dataset, we verify the effectiveness of the proposed approach by comparing our chatbot with several SOTA methods using automatic metrics, expert-based human judgements as well as user-based online evaluation.