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We present a dependency parser implemented as a single deep neural networkthat reads orthographic representations of words and directly generatesdependencies and their labels. Unlike typical approaches to parsing, the modeldoesn't require part-of-speech (POS) tagging of the sentences. With properregularization and additional supervision achieved with multitask learning wereach state-of-the-art performance on Slavic languages from the UniversalDependencies treebank: with no linguistic features other than characters, ourparser is as accurate as a transition- based system trained on perfect POStags.

Read, Tag, and Parse All at Once, or Fully-neural Dependency Parsing