Robustness Gym: Unifying the NLP Evaluation Landscape

arxiv(2021)

引用 119|浏览727
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摘要
Despite impressive performance on standard benchmarks, natural language processing (NLP) models are often brittle when deployed in real-world systems. In this work, we identify challenges with evaluating NLP systems and propose a solution in the form of Robustness Gym (RG),(1) a simple and extensible evaluation toolkit that unifies 4 standard evaluation paradigms: subpopulations, transformations, evaluation sets, and adversarial attacks. By providing a common platform for evaluation, RG enables practitioners to compare results from disparate evaluation paradigms with a single click, and to easily develop and share novel evaluation methods using a built-in set of abstractions. Robustness Gym is under active development and we welcome feedback & contributions from the community.
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关键词
nlp evaluation landscape,robustness
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