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A well-calibrated neural model produces confidence (probability outputs) closely approximated by the expected accuracy.
Calibration of encoder decoder models for neural machine translation
Aviral Kumar and Sunita Sarawagi. 2019 · 1903
Earlier work this paper cites.
Augmenting data with mixup for sentence classification: An empirical study
Hongyu Guo, Yongyi Mao, and Richong Zhang. 2019a · 1905
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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
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2013 · 2013
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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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Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht. 2015 · 2015
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Posterior calibration and exploratory analysis for natural language processing models
Khanh Nguyen and Brendan O’Connor. 2015 · 2015
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Visualizing and understanding neural models in NLP
Jiwei Li, Xinlei Chen, Eduard Hovy, and Dan Jurafsky. 2016 · 2016
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger. 2017 · 2017
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Quora question pairs
Shankar Iyer, Nikhil Dandekar, and Kornel Csernai. 2017 · 2017
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A continuously growing dataset of sentential paraphrases
Wuwei Lan, Siyu Qiu, Hua He, and Wei Xu. 2017 · 2017
Earlier work this paper cites.
Zero-shot sequence labeling: Transferring knowledge from sentences to tokens
Marek Rei and Anders Søgaard. 2018 · 2018
Earlier work this paper cites.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
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Swag: A large-scale adversarial dataset for grounded commonsense inference
Rowan Zellers, Yonatan Bisk, Roy Schwartz, and Yejin Choi. 2018 · 2018
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. 2018 · 2018
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Clinical concept extraction with contextual word embedding
Henghui Zhu, Ioannis C Paschalidis, and Amir M Tahmasebi. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Calibration of pre-trained transformers
Shrey Desai and Greg Durrett. 2020 · 2020
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Posterior calibrated training on sentence classification tasks
Taehee Jung, Dongyeop Kang, Hua Cheng, Lucas Mentch, and Thomas Schaaf. 2020 · 2020
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Calibrated language model fine-tuning for in- and out-of-distribution data
Lingkai Kong, Haoming Jiang, Yuchen Zhuang, Jie Lyu, Tuo Zhao, and Chao Zhang. 2020 · 2020
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Identifying mislabeled data using the area under the margin ranking
Geoff Pleiss, Tianyi Zhang, Ethan Elenberg, and Kilian Q Weinberger. 2020 · 2020
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Mixup-transformer: Dynamic data augmentation for NLP tasks
Lichao Sun, Congying Xia, Wenpeng Yin, Tingting Liang, Philip Yu, and Lifang He. 2020 · 2020
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Fine-tuning bidirectional encoder representations from transformers (bert)–based models on large-scale electronic health record notes: An empirical study
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When does label smoothing help?
Rafael Müller, Simon Kornblith, and Geoffrey E Hinton. 2019 · 2019
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On mixup training: Improved calibration and predictive uncertainty for deep neural networks
Sunil Thulasidasan, Gopinath Chennupati, Jeff A Bilmes, Tanmoy Bhattacharya, and Sarah Michalak. 2019 · 2019
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Manifold mixup: Better representations by interpolating hidden states
Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, and Yoshua Bengio. 2019 · 2019
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MixText: Linguistically-informed interpolation of hidden space for semi-supervised text classification
Jiaao Chen, Zichao Yang, and Diyi Yang. 2020 · 2020
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Mixup as locally linear out-of-manifold regularization
Hongyu Guo, Yongyi Mao, and Richong Zhang. 2019b
Cited in the paper.
Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Noah A Smith, and Yejin Choi. 2020 · 2020
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SeqMix: Augmenting active sequence labeling via sequence mixup
Rongzhi Zhang, Yue Yu, and Chao Zhang. 2020 · 2020
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Joint energy-based model training for better calibrated natural language understanding models
Tianxing He, Bryan McCann, Caiming Xiong, and Ehsan Hosseini-Asl. 2021 · 2021
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BatchMixup: Improving training by interpolating hidden states of the entire mini-batch
Wenpeng Yin, Huan Wang, Jin Qu, and Caiming Xiong. 2021 · 2021
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SSMix: Saliency-based span mixup for text classification
Soyoung Yoon, Gyuwan Kim, and Kyumin Park. 2021 · 2021
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Calibrating structured output predictors for natural language processing
Abhyuday Jagannatha and Hong Yu. 2020 · 2092
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