This report presents the results from the RuSimpleSentEval Shared Task conducted as a part of the Dialogue 2021 evaluation campaign. For the RSSE Shared Task, devoted to sentence simplification in Russian, a new middlescale dataset is created from scratch. It enumerates more than 3000 sentences sampled from popular Wikipedia pages. Each sentence is aligned with 2.2 simplified modifications, on average. The Shared Task implies sequenceto-sequence approaches: given an input complex sentence, a system should provide with its simplified version. A popular sentence simplification measure, SARI, is used to evaluate the system's performance.Fourteen teams participated in the Shared Task, submitting almost 350 runs involving different sentence simplification strategies. The Shared Task was conducted in two phases, with the public test phase allowing an unlimited number of submissions and the brief private test phase accepting one submission only. The post-evaluation phase remains open even after the end of private testing. The RSSE Shared Task has achieved its objective by providing a common ground for evaluating state-of-the-art models. We hope that the research community will benefit from the presented evaluation campaign.https://github.com/dialogue-evaluation/RuSimpleSentEval/.

In this paper we describe our submission to GramEval2020 competition on morphological tagging, lemmatization and dependency parsing. Our model uses biaffine attention over the BERT representations. The main feature of our work is the extensive usage of language model, tagger and parser fine-tuning on several distinct genres and the implementation of genre classifier. To deal with dataset idiosyncrasies we also extensively apply handwritten rules. Our model took second place in the overall model performance scoring 90.8 aggregate measure over all 4 tasks

The paper deals with elaborating different approaches to the machine processing of semantic sketches. It presents the pilot open corpus of semantic sketches. Different aspects of creating the sketches are discussed, as well as the tasks that the sketches can help to solve. Special attention is paid to the creation of the machine pro cessing tools for the corpus. For this purpose, the SemSketches2021 Shared Task was organized. The participants were given the anonymous sketches and a set of contexts containing the necessary predicates. During the Task, one had to assign the proper contexts to the corresponding sketches.

The purpose of this study is to evaluate the possibilities of authorship attribution methods for different segments of the Internet. We focus our efforts on Russian-language content. Since texts in different segments of the Internet have various features and characteristics, we collected datasets from different sections, such as channels messengers, blogs, news reviews and literary works.