  We explore whether it is possible to build lighter parsers, that are
statistically equivalent to their corresponding standard version, for a wide
set of languages showing different structures and morphologies. As testbed, we
use the Universal Dependencies and transition-based dependency parsers trained
on feed-forward networks. For these, most existing research assumes de facto
standard embedded features and relies on pre-computation tricks to obtain
speed-ups. We explore how these features and their size can be reduced and
whether this translates into speed-ups with a negligible impact on accuracy.
The experiments show that grand-daughter features can be removed for the
majority of treebanks without a significant (negative or positive) LAS
difference. They also show how the size of the embeddings can be notably
reduced.
