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We present an extensive evaluation of three recently proposed methods forcontextualized embeddings on 89 corpora in 54 languages of the UniversalDependencies 2.3 in three tasks: POS tagging, lemmatization, and dependencyparsing. Employing the BERT, Flair and ELMo as pretrained embedding inputs in astrong baseline of UDPipe 2.0, one of the best-performing systems of the CoNLL2018 Shared Task and an overall winner of the EPE 2018, we present a one-to-onecomparison of the three contextualized word embedding methods, as well as acomparison with word2vec-like pretrained embeddings and with end-to-endcharacter-level word embeddings. We report state-of-the-art results in allthree tasks as compared to results on UD 2.2 in the CoNLL 2018 Shared Task.

Evaluating Contextualized Embeddings on 54 Languages in POS Tagging, Lemmatization and Dependency Parsing