Semi-supervised Multitask Learning for Sequence Labeling
Table 3: Accuracy of different sequence labeling architectures on POS-tagging datasets.
['[EMPTY]', 'GENIA-POS DEV', 'GENIA-POS TEST', 'PTB-POS DEV', 'PTB-POS TEST', 'UD-ES DEV', 'UD-ES TEST', 'UD-FI DEV', 'UD-FI TEST']
While the performance improvements are small, they are consistent across all domains, languages and datasets. Application of dropout again provides a more robust model, and the language modeling cost improves the performance further. Even though the labels already offer a varied training objective, learning to predict the surrounding words at the same time has provided the model with additional general-purpose features.