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 and recognizing emotion causes. This survey starts by introducing ERC, elaborating on the challenges and opportunities pertaining to this task. It proceeds with a description of the main emotion taxonomies and methods to deal with subjectivity in annotations. It then describes Deep Learning methods relevant for ERC, word embeddings, and elaborates on the use of performance metrics for the task and methods to deal with the typically unbalanced ERC datasets. This is followed by a description and benchmark of key ERC works along with comprehensive tables comparing several works regarding their methods and performance across different datasets. 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：过去几年来,情感在对话中的认知(ERC)取得了巨大进步,但新的应用和实施方案带来了新的挑战和机遇,从利用对话背景、演讲人和情感动态建模,到解释常识表达、非正式语言和讽刺,解决实时情感研究中心的挑战,并承认情感原因。这次调查首先介绍情感研究中心,阐述与这项任务有关的挑战和机遇。接着介绍主要的情感分类和方法,在说明中处理主观性。然后介绍与情感研究中心相关的深学习方法、词嵌入,并阐述处理典型的不平衡的ERC数据集的任务和方法使用性能衡量标准,随后介绍和基准关键ERC工作,并附上综合表格,比较有关其方法和不同数据集性能的若干工作。调查强调了利用技术处理不平衡数据、探讨混合情感和将注解主题纳入学习阶段的好处。