TAPE: Assessing Few-shot Russian Language Understanding
| Autor: | 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 |
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| Rok vydání: | 2022 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| DOI: | 10.18653/v1/2022.findings-emnlp.183 |
| Popis: | 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. Comment: Accepted to EMNLP 2022 Findings |
| Databáze: | arXiv |
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