Dice Loss for Data-imbalanced NLP Tasks
Table 9: The effect of DL and DSC on sentiment classification tasks. BERT+CE refers to fine-tuning BERT and setting cross-entropy as the training objective.
['[EMPTY]', '[BOLD] SST-2', '[BOLD] SST-5']
For SST-5, BERT with CE achieves 55.57 in terms of accuracy, while DL and DSC perform slightly worse (54.63 and 55.19, respectively). Similar phenomenon is observed for SST-2. These results verify that the proposed dice loss is not accuracy-oriented, and should not be used for accuracy-oriented tasks.