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Transition-based and graph-based dependency parsers have previously beenshown to have complementary strengths and weaknesses: transition-based parsersexploit rich structural features but suffer from error propagation, whilegraph-based parsers benefit from global optimization but have restrictedfeature scope. In this paper, we show that, even though some details of thepicture have changed after the switch to neural networks and continuousrepresentations, the basic trade-off between rich features and globaloptimization remains essentially the same. Moreover, we show that deepcontextualized word embeddings, which allow parsers to pack information aboutglobal sentence structure into local feature representations, benefittransition-based parsers more than graph-based parsers, making the twoapproaches virtually equivalent in terms of both accuracy and error profile. Weargue that the reason is that these representations help prevent search errorsand thereby allow transition-based parsers to better exploit their inherentstrength of making accurate local decisions. We support this explanation by anerror analysis of parsing experiments on 13 languages.

Deep Contextualized Word Embeddings in Transition-Based and Graph-Based Dependency Parsing -- A Tale of Two Parsers Revisited