TAPE: Assessing Few-shot Russian Language Understanding
Taktasheva Ekaterina, Shavrina Tatiana, Fenogenova Alena, Shevelev Denis, Katricheva Nadezhda, Tikhonova Maria, Akhmetgareeva Albina, Zinkevich Oleg, Bashmakova Anastasiia, Iordanskaia Svetlana, Spiridonova Alena, Kurenshchikova Valentina, Artemova Ekaterina, Mikhailov Vladislav. Arxiv 2022
[Paper]
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 (tape-benchmark.com) to foster research on robust LMs that can generalize to new tasks when little to no supervision is available.
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