SyntaxNet Models for the CoNLL 2017 Shared Task
Table 1: Comparison to prior SyntaxNet models on UD v1.3. On average, we observe a 14.4% relative reduction in error (RRIE), or 3.47% absolute increase in LAS.
['Language', 'Parsey UAS', 'Parsey LAS', 'ParseySaurus UAS', 'ParseySaurus LAS', 'Language', 'Parsey UAS', 'Parsey LAS', 'ParseySaurus UAS', 'ParseySaurus LAS']
Since the test set is not available for the contest, we use v1.3 of the Universal Dependencies treebanks to compare to prior state-of-the-art on 52 languages. We observe that the new model outperforms the original SyntaxNet baselines, sometimes quite dramatically (e.g. on Latvian, by close to 12% absolute LAS.) We note that this is not an exhaustive experiment, and further study is warranted in the future. Nonethelss, these results show that the new baselines compare very favorably to at least one publicly available state-of-the-art baseline.