Denis Shevelev


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Read and Reason with MuSeRC and RuCoS: Datasets for Machine Reading Comprehension for Russian
Alena Fenogenova | Vladislav Mikhailov | Denis Shevelev
Proceedings of the 28th International Conference on Computational Linguistics

The paper introduces two Russian machine reading comprehension (MRC) datasets, called MuSeRC and RuCoS, which require reasoning over multiple sentences and commonsense knowledge to infer the answer. The former follows the design of MultiRC, while the latter is a counterpart of the ReCoRD dataset. The datasets are included in RussianSuperGLUE, the Russian general language understanding benchmark. We provide a comparative analysis and demonstrate that the proposed tasks are relatively more complex as compared to the original ones for English. Besides, performance results of human solvers and BERT-based models show that MuSeRC and RuCoS represent a challenge for recent advanced neural models. We thus hope to facilitate research in the field of MRC for Russian and prompt the study of multi-hop reasoning in a cross-lingual scenario.

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RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark
Tatiana Shavrina | Alena Fenogenova | Emelyanov Anton | Denis Shevelev | Ekaterina Artemova | Valentin Malykh | Vladislav Mikhailov | Maria Tikhonova | Andrey Chertok | Andrey Evlampiev
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

In this paper, we introduce an advanced Russian general language understanding evaluation benchmark – Russian SuperGLUE. Recent advances in the field of universal language models and transformers require the development of a methodology for their broad diagnostics and testing for general intellectual skills - detection of natural language inference, commonsense reasoning, ability to perform simple logical operations regardless of text subject or lexicon. For the first time, a benchmark of nine tasks, collected and organized analogically to the SuperGLUE methodology, was developed from scratch for the Russian language. We also provide baselines, human level evaluation, open-source framework for evaluating models, and an overall leaderboard of transformer models for the Russian language. Besides, we present the first results of comparing multilingual models in the translated diagnostic test set and offer the first steps to further expanding or assessing State-of-the-art models independently of language.