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The LyS-FASTPARSE team presents BIST-COVINGTON, a neural implementation ofthe Covington (2001) algorithm for non-projective dependency parsing. Thebidirectional LSTM approach by Kipperwasser and Goldberg (2016) is used totrain a greedy parser with a dynamic oracle to mitigate error propagation. Themodel participated in the CoNLL 2017 UD Shared Task. In spite of not using anyensemble methods and using the baseline segmentation and PoS tagging, theparser obtained good results on both macro-average LAS and UAS in the bigtreebanks category (55 languages), ranking 7th out of 33 teams. In the alltreebanks category (LAS and UAS) we ranked 16th and 12th. The gap between theall and big categories is mainly due to the poor performance on four parallelPUD treebanks, suggesting that some `suffixed' treebanks (e.g. Spanish-AnCora)perform poorly on cross-treebank settings, which does not occur with thecorresponding `unsuffixed' treebank (e.g. Spanish). By changing that, we obtainthe 11th best LAS among all runs (official and unofficial). The code is madeavailable at https://github.com/CoNLL-UD-2017/LyS-FASTPARSE

A non-projective greedy dependency parser with bidirectional LSTMs