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This paper introduces Bayesian uncertainty modeling using Stochastic Weight Averaging-Gaussian (SWAG) in Natural Language Understanding (NLU) tasks.
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Aleatory or epistemic? does it matter?
Armen Der Kiureghian and Ove Ditlevsen. 2009 · 2009
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SemEval-2012 task 6: A pilot on semantic textual similarity
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A SICK cure for the evaluation of compositional distributional semantic models
Marco Marelli, Stefano Menini, Marco Baroni, Luisa Bentivogli, Raffaella Bernardi, and Roberto Zamparelli. 2014 · 2014
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From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani. 2016 · 2016
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Averaging weights leads to wider optima and better generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson. 2018 · 2018
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2018 · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
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A simple baseline for bayesian uncertainty in deep learning
Wesley J Maddox, Pavel Izmailov, Timur Garipov, Dmitry P Vetrov, and Andrew Gordon Wilson. 2019 · 2019
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Inherent disagreements in human textual inferences
Ellie Pavlick and Tom Kwiatkowski. 2019 · 2019
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Human uncertainty makes classification more robust
Joshua C. Peterson, Ruairidh M. Battleday, Thomas L. Griffiths, and Olga Russakovsky. 2019 · 2019
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Testing the generalization power of neural network models across NLI benchmarks
Bayesian deep learning and a probabilistic perspective of generalization
Andrew G Wilson and Pavel Izmailov. 2020 · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020 · 2020
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How emotionally stable is ALBERT? testing robustness with stochastic weight averaging on a sentiment analysis task
Urja Khurana, Eric Nalisnick, and Antske Fokkens. 2021 · 2021
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Revisiting checkpoint averaging for neural machine translation
Yingbo Gao, Christian Herold, Zijian Yang, and Hermann Ney. 2022 · 2022
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When do flat minima optimizers work?
Jean Kaddour, Linqing Liu, Ricardo Silva, and Matt Kusner. 2022 · 2022
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Aarne Talman and Stergios Chatzikyriakidis. 2019 · 2019
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Uncertain natural language inference
Tongfei Chen, Zhengping Jiang, Adam Poliak, Keisuke Sakaguchi, and Benjamin Van Durme. 2020 · 2020
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What can we learn from collective human opinions on natural language inference data?
Yixin Nie, Xiang Zhou, and Mohit Bansal. 2020 · 2020
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Improving generalization of pre-trained language models via stochastic weight averaging
Peng Lu, Ivan Kobyzev, Mehdi Rezagholizadeh, Ahmad Rashid, Ali Ghodsi, and Phillippe Langlais. 2022 · 2022
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The “problem” of human label variation: On ground truth in data, modeling and evaluation
Barbara Plank. 2022 · 2022
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