An improved neural network model for joint POS tagging and dependency parsing
Table 2: Results on the test set. POS tagging accuracies are computed on all tokens. UAS and LAS are computed without punctuations. [∙]: the treebank was converted with the Stanford conversion toolkit v3.5.0. [⋆]: the treebank was converted with the head rules of Yamada and Matsumoto (2003). For both [∙] and [⋆], obtained parsing scores are just for reference, not for comparison.
['[BOLD] Model', '[BOLD] POS', '[BOLD] UAS', '[BOLD] LAS']
The last 6 rows present scores for joint models. Clearly, our model produces very competitive parsing results. Our model also does better than the previous transition-based joint models in Alberti et al.