Oleg Zinkevich


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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.