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