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RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark
Tatiana Shavrina; Alena Fenogenova; Anton Emelyanov; Denis Shevelev; Ekaterina Artemova; Valentin Malykh; Vladislav Mikhailov; Maria Tikhonova; Andrey Chertok; Andrey Evlampiev
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.
Code Repositories
Benchmarks
| Benchmark | Methodology | Metrics |
|---|---|---|
| common-sense-reasoning-on-parus | Baseline TF-IDF1.1 | Accuracy: 0.486 |
| common-sense-reasoning-on-parus | Human Benchmark | Accuracy: 0.982 |
| common-sense-reasoning-on-rucos | Human Benchmark | Average F1: 0.93 EM : 0.89 |
| common-sense-reasoning-on-rucos | Baseline TF-IDF1.1 | Average F1: 0.26 EM : 0.252 |
| common-sense-reasoning-on-rwsd | Baseline TF-IDF1.1 | Accuracy: 0.662 |
| common-sense-reasoning-on-rwsd | Human Benchmark | Accuracy: 0.84 |
| natural-language-inference-on-lidirus | Human Benchmark | MCC: 0.626 |
| natural-language-inference-on-lidirus | Baseline TF-IDF1.1 | MCC: 0.06 |
| natural-language-inference-on-rcb | Human Benchmark | Accuracy: 0.702 Average F1: 0.68 |
| natural-language-inference-on-rcb | Baseline TF-IDF1.1 | Accuracy: 0.441 Average F1: 0.301 |
| natural-language-inference-on-terra | Human Benchmark | Accuracy: 0.92 |
| natural-language-inference-on-terra | Baseline TF-IDF1.1 | Accuracy: 0.471 |
| question-answering-on-danetqa | Human Benchmark | Accuracy: 0.915 |
| question-answering-on-danetqa | Baseline TF-IDF1.1 | Accuracy: 0.621 |
| reading-comprehension-on-muserc | Baseline TF-IDF1.1 | Average F1: 0.587 EM : 0.242 |
| reading-comprehension-on-muserc | Human Benchmark | Average F1: 0.806 EM : 0.42 |
| word-sense-disambiguation-on-russe | Baseline TF-IDF1.1 | Accuracy: 0.57 |
| word-sense-disambiguation-on-russe | Human Benchmark | Accuracy: 0.805 |
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