Neural Probabilistic Model for Non-projective MST Parsing
Table 5: UAS and LAS on 14 treebanks from CoNLL shared tasks, together with several state-of-the-art parsers. “Best Published” includes the most accurate parsers in term of UAS among koo-EtAl:2010:EMNLP, martins-EtAl:2011:EMNLP1, martins:2013:Short, lei-EtAl:2014, zhang-EtAl:2014:EMNLP20143, zhang-mcdonald:2014, pitler-mcdonald:2015, ma-hovy:2015:EMNLP, and cheng-EtAl:2016:EMNLP2016.
['[EMPTY]', '[BOLD] Turbo UAS', '[BOLD] Tensor UAS', '[BOLD] RGB UAS', '[BOLD] In-Out UAS [LAS]', '[BOLD] Bi-Att UAS [LAS]', '[BOLD] +POS UAS [LAS]', '[BOLD] Full UAS [LAS]', '[BOLD] Best Published UAS', '[BOLD] Best Published LAS']
On average across 14 languages, our approach significantly outperforms all the baseline systems. It should be noted that the average UAS of our parser over the 14 languages is better than that of the “best published”, which are from different systems that achieved best results for different languages.