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RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark

2020-10-29EMNLP 2020Code Available1· sign in to hype

Tatiana Shavrina, Alena Fenogenova, Anton Emelyanov, Denis Shevelev, Ekaterina Artemova, Valentin Malykh, Vladislav Mikhailov, Maria Tikhonova, Andrey Chertok, Andrey Evlampiev

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Abstract

In this paper, we introduce an advanced Russian general language understanding evaluation benchmark -- RussianGLUE. 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 provide baselines, human level evaluation, an open-source framework for evaluating models (https://github.com/RussianNLP/RussianSuperGLUE), and an overall leaderboard of transformer models for the Russian language. Besides, we present the first results of comparing multilingual models in the adapted 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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Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
PARusHuman BenchmarkAccuracy0.98Unverified
PARusBaseline TF-IDF1.1Accuracy0.49Unverified
RuCoSBaseline TF-IDF1.1Average F10.26Unverified
RuCoSHuman BenchmarkAverage F10.93Unverified
RWSDBaseline TF-IDF1.1Accuracy0.66Unverified
RWSDHuman BenchmarkAccuracy0.84Unverified

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