Exploiting Multi-typed Treebanks for Parsing with Deep Multi-task Learning
Table 5: Low resource setup (3K tokens), evaluated with LAS.
['[EMPTY]', 'DE', 'ES', 'FR']
To verify the second issue, we consider a low resource setup following \newciteduong-EtAl:2015:EMNLP, where the target language has a small treebank (3K tokens). We train our models on identical sampled dataset shared by \newciteduong-EtAl:2015:EMNLP on DE, ES and FR. Although not the primary focus of this work, we find that SMTL and MTL can be significantly improved in low resource setting through weighted sampling of tasks during training. In this way, the two tasks are encouraged to converge at a similar rate.