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
Abstract
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.- Anthology ID:
- 2020.emnlp-main.381
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4717–4726
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.381
- DOI:
- 10.18653/v1/2020.emnlp-main.381
- Cite (ACL):
- Tatiana Shavrina, Alena Fenogenova, Emelyanov Anton, Denis Shevelev, Ekaterina Artemova, Valentin Malykh, Vladislav Mikhailov, Maria Tikhonova, Andrey Chertok, and Andrey Evlampiev. 2020. RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4717–4726, Online. Association for Computational Linguistics.
- Cite (Informal):
- RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark (Shavrina et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2020.emnlp-main.381.pdf
- Code
- RussianNLP/RussianSuperGLUE + additional community code
- Data
- DaNetQA, LiDiRus, MuSeRC, PARus, RCB, RWSD, RuCoS, TERRa, BoolQ, GLUE, RUSSE, SuperGLUE, WSC, decaNLP