SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems
Alex Wang ⋅ Yada Pruksachatkun ⋅ Nikita Nangia ⋅ Amanpreet Singh ⋅ Julian Michael ⋅ Felix Hill ⋅ Omer Levy ⋅ Samuel Bowman
Keywords:
Applications
Natural Language Processing
Algorithms -> Multitask and Transfer Learning; Algorithms
Representation Learning; Data, Challenges, Implementations, and So
2019 Poster
Abstract
In the last year, new models and methods for pretraining and transfer learning have driven striking performance improvements across a range of language understanding tasks. The GLUE benchmark, introduced a little over one year ago, offers a single-number metric that summarizes progress on a diverse set of such tasks, but performance on the benchmark has recently surpassed the level of non-expert humans, suggesting limited headroom for further research. In this paper we present SuperGLUE, a new benchmark styled after GLUE with a new set of more difficult language understanding tasks, a software toolkit, and a public leaderboard. SuperGLUE is available at https://super.gluebenchmark.com.
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