Dice Loss for Data-imbalanced NLP Tasks
Table 3: Experimental results for Chinese POS datasets including CTB5, CTB6 and UD1.4.
['[BOLD] Model', '[BOLD] CTB5  [BOLD] Prec.', '[BOLD] CTB5  [BOLD] Rec.', '[BOLD] CTB5  [BOLD] F1', '[BOLD] CTB6  [BOLD] Prec.', '[BOLD] CTB6  [BOLD] Rec.', '[BOLD] CTB6  [BOLD] F1', '[BOLD] UD1.4  [BOLD] Prec.', '[BOLD] UD1.4  [BOLD] Rec.', '[BOLD] UD1.4  [BOLD] F1']
As can be seen, the proposed DSC loss outperforms the best baseline results by a large margin, i.e., outperforming BERT-tagger by +1.86 in terms of F1 score on CTB5, +1.80 on CTB6 and +2.19 on UD1.4. As far as we are concerned, we are achieving SOTA performances on the three datasets. Focal loss only obtains a little performance improvement on CTB5 and CTB6, and the dice loss obtains huge gain on CTB5 but not on CTB6, which indicates the three losses are not consistently robust in solving the data imbalance issue.