The paper presents the results of GramEval 2020, a shared task on Russian morphological and syntactic processing. The objective is to process Russian texts starting from provided tokens to parts of speech (pos), grammatical features, lemmas, and labeled dependency trees. To encourage the multi-domain processing, five genres of Modern Russian are selected as test data: news, social media and electronic communication, wiki-texts, fiction, poetry; Middle Russian texts are used as the sixth test set. The data annotation follows the Universal Dependencies scheme. Unlike in many similar tasks, the collection of existing resources, the annotation of which is not perfectly harmonized, is provided for training, so the variability in annotations is a further source of difficulties. The main metric is the average accuracy of pos, features, and lemma tagging, and LAS. In this report, the organizers of GramEval 2020 overview the task, training and test data, evaluation methodology, submission routine, and participating systems. The approaches proposed by the participating systems and their results are reported and analyzed.

Orthographic and morphological heterogeneity of historical texts in premodern Slavic causes many difficulties in pos- and morphological tagging. Existing approaches to these tasks show state-of-the-art results without normalization, but they are still very sensitive to the properties of training data such as genre and origin. In this paper, we investigate to what extent the heterogeneity and size of the training corpus influence the quality of pos tagging and morphological analysis. We observe that UDpipe trained on different parts of the Middle Russian corpus demonstrates a boost in accuracy when using less training data. We resolve this paradox by analyzing the distribution of pos-tags and short words across subcorpora.