  Transition-based and graph-based dependency parsers have previously been
shown to have complementary strengths and weaknesses: transition-based parsers
exploit rich structural features but suffer from error propagation, while
graph-based parsers benefit from global optimization but have restricted
feature scope. In this paper, we show that, even though some details of the
picture have changed after the switch to neural networks and continuous
representations, the basic trade-off between rich features and global
optimization remains essentially the same. Moreover, we show that deep
contextualized word embeddings, which allow parsers to pack information about
global sentence structure into local feature representations, benefit
transition-based parsers more than graph-based parsers, making the two
approaches virtually equivalent in terms of both accuracy and error profile. We
argue that the reason is that these representations help prevent search errors
and thereby allow transition-based parsers to better exploit their inherent
strength of making accurate local decisions. We support this explanation by an
error analysis of parsing experiments on 13 languages.
