DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks
Table 4: The sizes of the sequence labeling datasets in our experiments, in terms of the number of tokens.
['[BOLD] Dataset', '[BOLD] Task', '[BOLD] Train', '[BOLD] Dev.', '[BOLD] Test']
We also evaluate the effectiveness of DropAttention on sequence labeling. We conducted experiments by following the same settings as yang2016multi. We use the following benchmark datasets in our experiments: Penn Treebank (PTB) POS tagging, CoNLL 2000 chunking, CoNLL 2003 English NER.