  This paper investigates how Natural Language Understanding (NLU) could be
applied in Emotion Recognition, a specific task in affective computing. We
finetuned different transformers language models (BERT, DistilBERT, RoBERTa,
XLNet, and ELECTRA) using a fine-grained emotion dataset and evaluating them in
terms of performance (f1-score) and time to complete.
