  We present a dependency parser implemented as a single deep neural network
that reads orthographic representations of words and directly generates
dependencies and their labels. Unlike typical approaches to parsing, the model
doesn't require part-of-speech (POS) tagging of the sentences. With proper
regularization and additional supervision achieved with multitask learning we
reach state-of-the-art performance on Slavic languages from the Universal
Dependencies treebank: with no linguistic features other than characters, our
parser is as accurate as a transition- based system trained on perfect POS
tags.
