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(Allen Institute For Artificial Intelligence & University Of Washington).pdf

1907.10641.pdf
Preview of (Allen Institute for Artificial Intelligence & University of Washington)
🔗 Source: arxiv.org
📊 Size: 1.05 MB
👤 Author: Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi
⬇️ Downloads: 110

Summary

Researchers introduce WINOGRANDE, a large-scale dataset of 44k problems inspired by the Winograd Schema Challenge, to investigate whether neural language models have truly acquired robust commonsense capabilities or rely on spurious biases in datasets. The best state-of-the-art methods on WINOGRANDE achieve 59.4-79.1% accuracy, which is 15-35% below human performance of 94.0%. The results also demonstrate the effectiveness of WINOGRANDE for transfer learning, achieving new state-of-the-art results on five related benchmarks, but raise concerns about overestimating the true capabilities of machine commonsense due to potential biases in existing datasets.

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Researchers introduce WINOGRANDE, a large-scale dataset of 44k problems inspired by the Winograd Schema Challenge, to investigate whether neural language...

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  • File Format: PDF
  • File Size: 1.05 MB
  • Pages: 9
  • Language: EN
  • Author: Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi
  • Total Downloads: 110
  • Last Updated: 18 hours ago

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