In this paper, we propose to use dice loss in replacement of the standard cross-entropy objective for data-imbalanced NLP tasks.
the number of tokens with tagging class O is 5 times as many as those with entity labels for the CoNLL03 dataset and 8 times for the OntoNotes5.0 dataset; Dataimbalanced issue is more severe for MRC tasks (Rajpurkar et al., 2016; Nguyen et al., 2016; Rajpurkar et al., 2018; Kocisky et al., 2018; Dasigi et al., 2019) with the value of negative-positive ratio being 50-200, which is due to the reason that the task of MRC is usually formalized as predicting the starting and ending indexes conditioned on the query and the context, and given a chunk of text of an arbitrary length, only two tokens are positive (or of interest) with all the rest being background.