Exploiting Multi-typed Treebanks for Parsing with Deep Multi-task Learning
Table 3: Parsing accuracies of Multilingual (Univ→Univ). Significance tests with MaltEval yield p-values < 0.01 for (MTL vs. Sup) on all languages.
['[EMPTY]', 'Multilingual (Univ → Univ) Sup', 'Multilingual (Univ → Univ) Sup', 'Multilingual (Univ → Univ)  \\textsc  [ITALIC] CasEN', 'Multilingual (Univ → Univ)  \\textsc  [ITALIC] CasEN', 'Multilingual (Univ → Univ)  \\textsc  [ITALIC] SMTLEN', 'Multilingual (Univ → Univ)  \\textsc  [ITALIC] SMTLEN', 'Multilingual (Univ → Univ)  \\textsc  [ITALIC] MTLEN', 'Multilingual (Univ → Univ)  \\textsc  [ITALIC] MTLEN']
Cas yields slightly better performance than Sup, especially for SV (+1.52% UAS and +2.04% LAS), indicating that pre-training with EN training data indeed provides a better initialization of the parameters for cascaded training. SMTL in turn outperforms Cas overall (comparable for IT), which implies that training two treebanks jointly helps even with an unique model.