Exploring Transformers in Emotion Recognition: a comparison of BERT, DistillBERT, RoBERTa, XLNet and ELECTRA
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.
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Methods
Adam •
Attention Dropout •
BERT •
BPE •
Dense Connections •
DistilBERT •
Dropout •
GELU •
Layer Normalization •
Linear Layer •
Linear Warmup With Linear Decay •
Multi-Head Attention •
Residual Connection •
RoBERTa •
Scaled Dot-Product Attention •
SentencePiece •
Softmax •
Weight Decay •
WordPiece •
XLNet
