On Multilingual Training of Neural Dependency Parsers
Table 1: Baseline results of single language models from UD v1.3. Our models use only orthographic representations of tokenized words during inference and work without a separate POS tagger. Ammar et al. [2] uses version 1.2 of UD and uses gold language ids and predicted coarse tags. SyntaxNet[3, 1] works on predicted POS tags, while ParseySaurus[1] uses word spellings.
['language', '#sentences', 'Ours UAS', 'Ours LAS', 'SyntaxNet UAS', 'SyntaxNet LAS', 'Ammar et al. LAS', 'ParseySaurus UAS', 'ParseySaurus LAS']
] learning rule with epsilon annealed from 1e-8 to 1e-12 and adaptive gradient Experiments are early-stopped on validation set Unlabeled Attachment Score (UAS) score.