Empathy is a social skill that indicates an individual's ability to
understand others. Over the past few years, empathy has drawn attention from
various disciplines, including but not limited to Affective Computing,
Cognitive Science and Psychology. Empathy is a context-dependent term; thus,
detecting or recognising empathy has potential applications in society,
healthcare and education. Despite being a broad and overlapping topic, the
avenue of empathy detection studies leveraging Machine Learning remains
underexplored from a holistic literature perspective. To this end, we
systematically collect and screen 801 papers from 10 well-known databases and
analyse the selected 54 papers. We group the papers based on input modalities
of empathy detection systems, i.e., text, audiovisual, audio and physiological
signals. We examine modality-specific pre-processing and network architecture
design protocols, popular dataset descriptions and availability details, and
evaluation protocols. We further discuss the potential applications, deployment
challenges and research gaps in the Affective Computing-based empathy domain,
which can facilitate new avenues of exploration. We believe that our work is a
stepping stone to developing a privacy-preserving and unbiased empathic system
inclusive of culture, diversity and multilingualism that can be deployed in
practice to enhance the overall well-being of human life.

我们系统地收集并筛选了 10 个著名数据库中的 801 篇论文，并对选中的 54 篇论文进行了分析。我们将这些论文根据共情检测系统的输入模态（文本、视听、音频和生理信号）进行分组，并对模态特定的预处理和网络架构设计协议、常用数据集描述和可用性细节以及评估协议进行了研究。我们进一步讨论了基于情感计算的共情领域的潜在应用、部署挑战和研究空白，这可以促进新的研究方向。我们相信我们的工作是发展一个包含隐私保护和无偏见、具有文化、多样性和多语言性的共情系统的垫脚石，可以在实践中提高人类生活的整体福祉。

使用文本、视听、音频或生理信号进行机器学习的共情检测

Empathy Detection Using Machine Learning on Text, Audiovisual, Audio or  Physiological Signals

Computational detection and understanding of empathy is an important factor
in advancing human-computer interaction. Yet to date, text-based empathy
prediction has the following major limitations: It underestimates the
psychological complexity of the phenomenon, adheres to a weak notion of ground
truth where empathic states are ascribed by third parties, and lacks a shared
corpus. In contrast, this contribution presents the first publicly available
gold standard for empathy prediction. It is constructed using a novel
annotation methodology which reliably captures empathy assessments by the
writer of a statement using multi-item scales. This is also the first
computational work distinguishing between multiple forms of empathy, empathic
concern, and personal distress, as recognized throughout psychology. Finally,
we present experimental results for three different predictive models, of which
a CNN performs the best.

本文提出了第一个公开可用的用于检测共情能力预测的标准，采用单项量表的方法可靠捕捉了作者对共情能力的评估，同时也是第一个区分多种共情形式的计算模型，实验表明 CNN 模型表现最佳。