CLApr 5, 2021
Exploring Transformers in Emotion Recognition: a comparison of BERT, DistillBERT, RoBERTa, XLNet and ELECTRA
arXiv:2104.02041v151 citations
Originality Synthesis-oriented
AI Analysis
This work addresses emotion recognition for affective computing applications, but it is incremental as it applies existing methods to a specific task.
The paper tackled emotion recognition by fine-tuning transformer models like BERT and RoBERTa on a fine-grained dataset, achieving results such as an F1-score of 0.85 for RoBERTa and faster training times for DistilBERT.
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.