Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks
Table 3: Comparison with state-of-the-art results (%).
['Model', 'CoNLL 2000', 'CoNLL 2003', 'Spanish', 'Dutch', 'PTB 2003']
Since there are no standard data splits on the Genia and Twitter corpora, we do not include these datasets into our comparison. The results for CoNLL 2000 chunking, CoNLL 2003 NER, and PTB POS tagging are obtained by transfer learning between the three tasks, i.e., transferring from two tasks to the other. The results for Spanish and Dutch NER are obtained with transfer learning between the NER datasets in three languages (English, Spanish, and Dutch). First, our transfer learning approach achieves new state-of-the-art results on all the considered benchmark datasets except PTB POS tagging, which indicates that transfer learning can still improve the performance even on datasets with relatively abundant labels.