Transfer Learning for Sequences via Learning to Collocate
Table 5: Performance over POS tagging and NER.
['Task', 'Source', 'Target', 'HRN', 'FLORS', 'LSTM', 'CCT', 'ART']
From the table, we see that the performances of all tasks are improved by ART. For example, when transferring from PTB to Twitter/0.1, ART outperforms HRN by 2.2%. ART performs the best among all competitors in almost all cases. This verifies the effectiveness of ART w.r.t. sequence labeling. Note that FLORS is independent of the target domain. If the training corpus of the target domain is quite rare (Twitter/0.01), FLORS performs better. But with richer training data of the target domain (Twitter/0.1), ART outperforms FLORS.