Maria Tikhonova


2022

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TAPE: Assessing Few-shot Russian Language Understanding
Ekaterina Taktasheva | Tatiana Shavrina | Alena Fenogenova | Denis Shevelev | Nadezhda Katricheva | Maria Tikhonova | Albina Akhmetgareeva | Oleg Zinkevich | Anastasiia Bashmakova | Svetlana Iordanskaia | Alena Spiridonova | Valentina Kurenshchikova | Ekaterina Artemova | Vladislav Mikhailov
Findings of the Association for Computational Linguistics: EMNLP 2022

Recent advances in zero-shot and few-shot learning have shown promise for a scope of research and practical purposes. However, this fast-growing area lacks standardized evaluation suites for non-English languages, hindering progress outside the Anglo-centric paradigm. To address this line of research, we propose TAPE (Text Attack and Perturbation Evaluation), a novel benchmark that includes six more complex NLU tasks for Russian, covering multi-hop reasoning, ethical concepts, logic and commonsense knowledge. The TAPE’s design focuses on systematic zero-shot and few-shot NLU evaluation: (i) linguistic-oriented adversarial attacks and perturbations for analyzing robustness, and (ii) subpopulations for nuanced interpretation. The detailed analysis of testing the autoregressive baselines indicates that simple spelling-based perturbations affect the performance the most, while paraphrasing the input has a more negligible effect. At the same time, the results demonstrate a significant gap between the neural and human baselines for most tasks. We publicly release TAPE (https://tape-benchmark.com) to foster research on robust LMs that can generalize to new tasks when little to no supervision is available.

2020

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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.