  Many NLP tasks such as tagging and machine reading comprehension are faced
with the severe data imbalance issue: negative examples significantly outnumber
positive examples, and the huge number of background examples (or easy-negative
examples) overwhelms the training. The most commonly used cross entropy (CE)
criteria is actually an accuracy-oriented objective, and thus creates a
discrepancy between training and test: at training time, each training instance
contributes equally to the objective function, while at test time F1 score
concerns more about positive examples. In this paper, we propose to use dice
loss in replacement of the standard cross-entropy objective for data-imbalanced
NLP tasks. Dice loss is based on the Sorensen-Dice coefficient or Tversky
index, which attaches similar importance to false positives and false
negatives, and is more immune to the data-imbalance issue. To further alleviate
the dominating influence from easy-negative examples in training, we propose to
associate training examples with dynamically adjusted weights to deemphasize
easy-negative examples.Theoretical analysis shows that this strategy narrows
down the gap between the F1 score in evaluation and the dice loss in training.
With the proposed training objective, we observe significant performance boost
on a wide range of data imbalanced NLP tasks. Notably, we are able to achieve
SOTA results on CTB5, CTB6 and UD1.4 for the part of speech tagging task; SOTA
results on CoNLL03, OntoNotes5.0, MSRA and OntoNotes4.0 for the named entity
recognition task; along with competitive results on the tasks of machine
reading comprehension and paraphrase identification.
