Semi-supervised Multitask Learning for Sequence Labeling
Table 2: Performance of alternative sequence labeling architectures on NER and chunking datasets, measured using CoNLL standard entity-level F1 score.
['[EMPTY]', 'CoNLL-00 DEV', 'CoNLL-00 TEST', 'CoNLL-03 DEV', 'CoNLL-03 TEST', 'CHEMDNER DEV', 'CHEMDNER TEST', 'JNLPBA DEV', 'JNLPBA TEST']
On these tasks, the application of dropout provides a consistent improvement – applying some variance onto the input embeddings results in more robust models for NER and chunking. The addition of the language modeling objective consistently further improves performance on all benchmarks.