While Emotion Recognition in Conversations (ERC) has seen a tremendous advancement in the last few years, new applications and implementation scenarios present novel challenges and opportunities. These range from leveraging the conversational context, speaker and emotion dynamics modelling, to interpreting common sense expressions, informal language and sarcasm, addressing challenges of real time ERC, recognizing emotion causes, different taxonomies across datasets, multilingual ERC to interpretability. This survey starts by introducing ERC, elaborating on the challenges and opportunities pertaining to this task. It proceeds with a description of the emotion taxonomies and a variety of ERC benchmark datasets employing such taxonomies. This is followed by descriptions of the most prominent works in ERC with explanations of the Deep Learning architectures employed. Then, it provides advisable ERC practices towards better frameworks, elaborating on methods to deal with subjectivity in annotations and modelling and methods to deal with the typically unbalanced ERC datasets. Finally, it presents systematic review tables comparing several works regarding the methods used and their performance. The survey highlights the advantage of leveraging techniques to address unbalanced data, the exploration of mixed emotions and the benefits of incorporating annotation subjectivity in the learning phase.

翻译：尽管对话情感识别（ERC）在近年来取得了显著进展，但新应用与实现场景仍带来诸多挑战与机遇。这些挑战涉及对话上下文利用、说话者与情感动态建模、常识表达与非正式语言及反讽的解读、实时ERC的应对、情感成因识别、跨数据集的不同分类体系、多语言ERC以及可解释性等议题。本综述首先介绍ERC，详细阐述该任务面临的挑战与机遇；继而描述情感分类体系及采用此类体系的多种ERC基准数据集。随后，梳理ERC领域最具代表性的研究成果，阐释所采用的深度学习架构，并针对更优框架提出可循的ERC实践规范，重点说明应对标注与建模中的主观性及典型非平衡ERC数据集的处理方法。最后，通过系统性对比表格，呈现多项研究在方法运用及性能表现上的差异。本综述强调应对非平衡数据的技术优势、混合情感探索的价值，以及在模型学习阶段纳入标注主观性的益处。