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RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmarkстатья
- Авторы: Shavrina Tatiana, Fenogenova Alena, Anton Emelyanov, Shevelev Denis, Artemova Ekaterina, Malykh Valentin, Mikhailov Vladislav, Tikhonova Maria, Chertok Andrey, Evlampiev Andrey
- Сборник: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Год издания: 2020
- Место издания: Association for Computational Linguistics Online
- Первая страница: 4717
- Последняя страница: 4726
- DOI: 10.18653/v1/2020.emnlp-main.381
- Аннотация: 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.
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