Empathy is a vital factor that contributes to mutual understanding, and joint problem-solving. In recent years, a growing number of studies have recognized the benefits of empathy and started to incorporate empathy in conversational systems. We refer to this topic as empathetic conversational systems. To identify the critical gaps and future opportunities in this topic, this paper examines this rapidly growing field using five review dimensions: (i) conceptual empathy models and frameworks, (ii) adopted empathy-related concepts, (iii) datasets and algorithmic techniques developed, (iv) evaluation strategies, and (v) state-of-the-art approaches. The findings show that most studies have centered on the use of the EMPATHETICDIALOGUES dataset, and the text-based modality dominates research in this field. Studies mainly focused on extracting features from the messages of the users and the conversational systems, with minimal emphasis on user modeling and profiling. Notably, studies that have incorporated emotion causes, external knowledge, and affect matching in the response generation models, have obtained significantly better results. For implementation in diverse real-world settings, we recommend that future studies should address key gaps in areas of detecting and authenticating emotions at the entity level, handling multimodal inputs, displaying more nuanced empathetic behaviors, and encompassing additional dialogue system features.

翻译：近些年来,越来越多的研究已经认识到共鸣的好处,并开始将共鸣纳入对谈系统。我们把这个题目称为 " 同情对话系统 " 。我们把这个题目称为 " 同情对话系统 " 。为了找出这个专题中的关键差距和未来的机会,本文件利用五个审查层面来研究这个迅速增长的领域:(一) 概念共鸣模式和框架,(二) 采用与共鸣有关的概念,(三) 开发的数据集和算法技术,(四) 评价战略,以及(五) 最先进的方法。研究结果显示,大多数研究都集中在使用EMPATHITITITALOUUES数据集, 以及基于文本的模式主导了这一领域的研究。研究主要侧重于从用户和对话系统的信息中提取特征,而很少强调用户的建模和特征。值得注意的是,已经将情感原因、外部知识以及影响反应生成模型的匹配性研究取得了显著的更好结果。为了在不同的现实世界环境中实施,我们建议未来系统研究应该研究如何弥补实体在更真实的情感方面进行更多的研究。