  We evaluate three leading dependency parser systems from different paradigms
on a small yet diverse subset of languages in terms of their
accuracy-efficiency Pareto front. As we are interested in efficiency, we
evaluate core parsers without pretrained language models (as these are
typically huge networks and would constitute most of the compute time) or other
augmentations that can be transversally applied to any of them. Biaffine
parsing emerges as a well-balanced default choice, with sequence-labelling
parsing being preferable if inference speed (but not training energy cost) is
the priority.
