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We applied the T5 sequence-to-sequence model to tackle the AI2 WinoGrande Challenge by decomposing each example into two input text strings, each containing a hypothesis, and using the probabilities assigned to the "entailment" token as a score of the hypothesis.
Understanding natural language
T. Winograd · 1972
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A broad-coverage challenge corpus for sentence understanding through inference
A. Williams, N. Nangia, and S. Bowman · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2019
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M. Lewis, Y. Liu, N. Goyal, M. Ghazvininejad, A. Mohamed, O. Levy, V. Stoyanov, and L. Zettlemoyer · 2019
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RoBERTa: A robustly optimized BERT pretraining approach
Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov · 2019
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Exploring the limits of transfer learning with a unified text-to-text transformer
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu · 2019
Cited alongside, same era.
WinoGrande: An adversarial Winograd schema challenge at scale
K. Sakaguchi, R. L. Bras, C. Bhagavatula, and Y. Choi · 2019
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XLNet: Generalized autoregressive pretraining for language understanding
Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. Salakhutdinov, and Q. V. Le · 2019
Later among the works it cites.
Document ranking with a pretrained sequence-to-sequence model
R. Nogueira, Z. Jiang, and J. Lin · 2020
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