Dice Loss for Data-imbalanced NLP Tasks
Table 6: Experimental results for MRC task.
['[BOLD] Model', '[BOLD] SQuAD v1.1  [BOLD] EM', '[BOLD] SQuAD v1.1  [BOLD] F1', '[BOLD] SQuAD v2.0  [BOLD] EM', '[BOLD] SQuAD v2.0  [BOLD] F1', '[BOLD] QuoRef  [BOLD] EM', '[BOLD] QuoRef  [BOLD] F1']
With either BERT or XLNet, our proposed DSC loss obtains significant performance boost on both EM and F1. For SQuADv1.1, our proposed method outperforms XLNet by +1.25 in terms of F1 score and +0.84 in terms of EM. For SQuAD v2.0, the proposed method achieves 87.65 on EM and 89.51 on F1. On QuoRef, the proposed method surpasses XLNet by +1.46 on EM and +1.41 on F1.