1 research outputs found
Deep Emotion Recognition in Textual Conversations: A Survey
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