Dice Loss for Data-imbalanced NLP Tasks
Table 10: The effect of hyperparameters in Tversky Index. We set β=1−α and thus we only list α here.
['[ITALIC] α', '[BOLD] Chinese Onto4.0', '[BOLD] English QuoRef']
In this subsection, we explore the effect of hyperparameters (i.e., α and β) in TI to test how they manipulate the tradeoff. We conduct experiments on the Chinese OntoNotes4.0 NER dataset and English QuoRef MRC dataset. The highest F1 on Chinese OntoNotes4.0 is 84.67 when α is set to 0.6 while for QuoRef, the highest F1 is 68.44 when α is set to 0.4. In addition, we can observe that the performance varies a lot as α changes in distinct datasets, which shows that the hyperparameters α,β acturally play an important role in TI.