# Artificial Empathy Classification: A Survey of Deep Learning Techniques, Datasets, and Evaluation Scales

arXiv: https://arxiv.org/abs/2310.00010 · HTML: https://mlsanity.com/paper/2310.00010/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 133 references that are in the map.
- First posted on arXiv 4 Sep 2023.

## Builds on (most cited first, 21 of 21)

- [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)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [Recipes for building an open-domain chatbot](https://mlsanity.com/paper/2004.13637/) (2020)
- [How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation](https://mlsanity.com/paper/1603.08023/) (2016)
- [SoundNet: Learning Sound Representations from Unlabeled Video](https://mlsanity.com/paper/1610.09001/) (2016)
- [A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support](https://mlsanity.com/paper/2009.08441/) (2020)
- [MoEL: Mixture of Empathetic Listeners](https://mlsanity.com/paper/1908.07687/) (2019)
- [Improving Contextual Coherence in Variational Personalized and Empathetic Dialogue Agents](https://mlsanity.com/paper/2202.05971/) (2022)
- [Multi-Resolution A*](https://mlsanity.com/paper/2004.06684/) (2020)
- [Intrinsically motivated reinforcement learning for human-robot interaction in the real-world](https://mlsanity.com/paper/1804.05259/) (2018)
- [The OMG-Empathy Dataset: Evaluating the Impact of Affective Behavior in Storytelling](https://mlsanity.com/paper/1908.11706/) (2019)
- [Evaluating Empathy in Artificial Agents](https://mlsanity.com/paper/1908.05341/) (2019)
- [Modeling User Empathy Elicited by a Robot Storyteller](https://mlsanity.com/paper/2107.14345/) (2021)
- [A Multimodal LSTM for Predicting Listener Empathic Responses Over Time](https://mlsanity.com/paper/1812.04891/) (2018)
- [An Empathetic AI Coach for Self-Attachment Therapy](https://mlsanity.com/paper/2209.08316/) (2022)
- [Towards Empathetic Human-Robot Interactions](https://mlsanity.com/paper/1605.04072/) (2016)

## Built on by (most cited first, 2 of 2)

- [Assessing Empathy in Large Language Models with Real-World Physician-Patient Interactions](https://mlsanity.com/paper/2405.16402/) (2024)
- [Rational Sensibility: LLM Enhanced Empathetic Response Generation Guided by Self-presentation Theory](https://mlsanity.com/paper/2312.08702/) (2023)

## Abstract (arXiv)

From the last decade, researchers in the field of machine learning (ML) and assistive developmental robotics (ADR) have taken an interest in artificial empathy (AE) as a possible future paradigm for human-robot interaction (HRI). Humans learn empathy since birth, therefore, it is challenging to instill this sense in robots and intelligent machines. Nevertheless, by training over a vast amount of data and time, imitating empathy, to a certain extent, can be possible for robots. Training techniques for AE, along with findings from the field of empathetic AI research, are ever-evolving. The standard workflow for artificial empathy consists of three stages: 1) Emotion Recognition (ER) using the retrieved features from video or textual data, 2) analyzing the perceived emotion or degree of empathy to choose the best course of action, and 3) carrying out a response action. Recent studies that show AE being used with virtual agents or robots often include Deep Learning (DL) techniques. For instance, models like VGGFace are used to conduct ER. Semi-supervised models like Autoencoders generate the corresponding emotional states and behavioral responses. However, there has not been any study that presents an independent approach for evaluating AE, or the degree to which a reaction was empathetic. This paper aims to investigate and evaluate existing works for measuring and evaluating empathy, as well as the datasets that have been collected and used so far. Our goal is to highlight and facilitate the use of state-of-the-art methods in the area of AE by comparing their performance. This will aid researchers in the area of AE in selecting their approaches with precision.