# Use of a Taxonomy of Empathetic Response Intents to Control and Interpret Empathy in Neural Chatbots

arXiv: https://arxiv.org/abs/2305.10096 · HTML: https://mlsanity.com/paper/2305.10096/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 39 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 19 of 19)

- [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)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Neurosymbolic AI: The 3rd Wave](https://mlsanity.com/paper/2012.05876/) (2020)
- [A Neural Conversational Model](https://mlsanity.com/paper/1506.05869/) (2015)
- [Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory](https://mlsanity.com/paper/1704.01074/) (2017)
- [Neural Approaches to Conversational AI](https://mlsanity.com/paper/1809.08267/) (2018)
- [Neural Responding Machine for Short-Text Conversation](https://mlsanity.com/paper/1503.02364/) (2015)
- [A Neural Network Approach to Context-Sensitive Generation of Conversational Responses](https://mlsanity.com/paper/1506.06714/) (2015)
- [MojiTalk: Generating Emotional Responses at Scale](https://mlsanity.com/paper/1711.04090/) (2017)
- [A Taxonomy of Empathetic Response Intents in Human Social Conversations](https://mlsanity.com/paper/2012.04080/) (2020)
- [A Controllable Model of Grounded Response Generation](https://mlsanity.com/paper/2005.00613/) (2020)
- [Controlling Dialogue Generation with Semantic Exemplars](https://mlsanity.com/paper/2008.09075/) (2020)
- [Policy-Driven Neural Response Generation for Knowledge-Grounded Dialogue Systems](https://mlsanity.com/paper/2005.12529/) (2020)
- [Touch Your Heart: A Tone-aware Chatbot for Customer Care on Social Media](https://mlsanity.com/paper/1803.02952/) (2018)
- [Deep Reinforcement Learning For Modeling Chit-Chat Dialog With Discrete Attributes](https://mlsanity.com/paper/1907.02848/) (2019)
- [Learning to Plan and Realize Separately for Open-Ended Dialogue Systems](https://mlsanity.com/paper/2009.12506/) (2020)
- [Fine-grained Emotion and Intent Learning in Movie Dialogues](https://mlsanity.com/paper/2012.13624/) (2020)
- [Investigation of Sentiment Controllable Chatbot](https://mlsanity.com/paper/2007.07196/) (2020)

## Abstract (arXiv)

A recent trend in the domain of open-domain conversational agents is enabling them to converse empathetically to emotional prompts. Current approaches either follow an end-to-end approach or condition the responses on similar emotion labels to generate empathetic responses. But empathy is a broad concept that refers to the cognitive and emotional reactions of an individual to the observed experiences of another and it is more complex than mere mimicry of emotion. Hence, it requires identifying complex human conversational strategies and dynamics in addition to generic emotions to control and interpret empathetic responding capabilities of chatbots. In this work, we make use of a taxonomy of eight empathetic response intents in addition to generic emotion categories in building a dialogue response generation model capable of generating empathetic responses in a controllable and interpretable manner. It consists of two modules: 1) a response emotion/intent prediction module; and 2) a response generation module. We propose several rule-based and neural approaches to predict the next response's emotion/intent and generate responses conditioned on these predicted emotions/intents. Automatic and human evaluation results emphasize the importance of the use of the taxonomy of empathetic response intents in producing more diverse and empathetically more appropriate responses than end-to-end models.