# Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset

arXiv: https://arxiv.org/abs/1811.00207 · HTML: https://mlsanity.com/paper/1811.00207/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2018.

## Built on by (most cited first, 18 of 18)

- [MISC: A MIxed Strategy-Aware Model Integrating COMET for Emotional Support Conversation](https://mlsanity.com/paper/2203.13560/) (2022)
- [I Think, Therefore I am: Benchmarking Awareness of Large Language Models Using AwareBench](https://mlsanity.com/paper/2401.17882/) (2024)
- [Beyond Emotion Recognition: A Multi-Turn Multimodal Emotion Understanding and Reasoning Benchmark](https://mlsanity.com/paper/2508.16859/) (2025)
- [Cause-Aware Empathetic Response Generation via Chain-of-Thought Fine-Tuning](https://mlsanity.com/paper/2408.11599/) (2024)
- [Empathetic Dialogue Generation with Pre-trained RoBERTa-GPT2 and External Knowledge](https://mlsanity.com/paper/2109.03004/) (2021)
- [ASEM: Enhancing Empathy in Chatbot through Attention-based Sentiment and Emotion Modeling](https://mlsanity.com/paper/2402.16194/) (2024)
- [PoKE: Prior Knowledge Enhanced Emotional Support Conversation with Latent Variable](https://mlsanity.com/paper/2210.12640/) (2022)
- [Put Chatbot into Its Interlocutor's Shoes: New Framework to Learn Chatbot Responding with Intention](https://mlsanity.com/paper/2103.16429/) (2021)
- [Improving Linguistic Diversity of Large Language Models with Possibility Exploration Fine-Tuning](https://mlsanity.com/paper/2412.03343/) (2024)
- [Thanos: Enhancing Conversational Agents with Skill-of-Mind-Infused Large Language Model](https://mlsanity.com/paper/2411.04496/) (2024)
- [StreamSide: A Fully-Customizable Open-Source Toolkit for Efficient Annotation of Meaning Representations](https://mlsanity.com/paper/2109.09853/) (2021)
- [Towards a Sentiment-Aware Conversational Agent](https://mlsanity.com/paper/2207.11774/) (2022)
- [Mind the Knowledge Gap: A Survey of Knowledge-enhanced Dialogue Systems](https://mlsanity.com/paper/2212.09252/) (2022)
- [Empathetic Response Generation with State Management](https://mlsanity.com/paper/2205.03676/) (2022)
- [Emotion Recognition in Conversation using Probabilistic Soft Logic](https://mlsanity.com/paper/2207.07238/) (2022)
- [Widely Interpretable Semantic Representation: Frameless Meaning Representation for Broader Applicability](https://mlsanity.com/paper/2309.06460/) (2023)
- [Incorporating Different Verbal Cues to Improve Text-Based Computer-Delivered Health Messaging](https://mlsanity.com/paper/2404.13633/) (2024)
- [An Interdisciplinary Review of Commonsense Reasoning and Intent Detection](https://mlsanity.com/paper/2506.14040/) (2025)

## Abstract (arXiv)

One challenge for dialogue agents is recognizing feelings in the conversation partner and replying accordingly, a key communicative skill. While it is straightforward for humans to recognize and acknowledge others' feelings in a conversation, this is a significant challenge for AI systems due to the paucity of suitable publicly-available datasets for training and evaluation. This work proposes a new benchmark for empathetic dialogue generation and EmpatheticDialogues, a novel dataset of 25k conversations grounded in emotional situations. Our experiments indicate that dialogue models that use our dataset are perceived to be more empathetic by human evaluators, compared to models merely trained on large-scale Internet conversation data. We also present empirical comparisons of dialogue model adaptations for empathetic responding, leveraging existing models or datasets without requiring lengthy re-training of the full model.