Emotion Recognition in Conversations (ERC) is a key step towards successful human–machine interaction. While the field has seen 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, and interpretability. This survey starts by introducing ERC, elaborating on the challenges and opportunities of 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 comparing the most prominent works in ERC with explanations of the neural 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. Benchmarking these works highlights resorting to pre-trained Transformer Language Models to extract utterance representations, using Gated and Graph Neural Networks to model the interactions between these utterances, and leveraging Generative Large Language Models to tackle ERC within a generative framework. This survey emphasizes 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.

This paper focuses on the tasks of recovering capitalization and punctuation marks from texts without that information, such as spoken transcripts, produced by automatic speech recognition systems. These two practical rich transcription tasks were performed using the same discriminative approach, based on maximum entropy, suitable for on-the-fly usage. Reported experiments were conducted both over Portuguese and English broadcast news data. Both force aligned and automatic transcripts were used, allowing to measure the impact of the speech recognition errors. Capitalized words and named entities are intrinsically related, and are influenced by time variation effects. For that reason, the so-called language dynamics have been addressed for the capitalization task. Language adaptation results indicate, for both languages, that the capitalization performance is affected by the temporal distance between the training and testing data. In what regards the punctuation task, this paper covers the three most frequent punctuation marks: full stop, comma, and question marks. Different methods were explored for improving the baseline results for full stop and comma. The first uses punctuation information extracted from large written corpora. The second applies different levels of linguistic structure, including lexical, prosodic, and speaker related features. The comma detection improved significantly in the first method, thus indicating that it depends more on lexical features. The second method provided even better results, for both languages and both punctuation marks, best results being achieved mainly for full stop. As for question marks, there is a small gain, but differences are not very significant, due to the relatively small number of question marks in the corpora.