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Modern deep models for summarization attains impressive benchmark performance, but they are prone to generating miscalibrated predictive uncertainty.
Batchensemble: an alternative approach to efficient ensemble and lifelong learning
Yeming Wen, Dustin Tran, and Jimmy Ba. 2020 · 2002
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Probabilistic forecasts, calibration and sharpness
Tilmann Gneiting, Fadoua Balabdaoui, and Adrian E Raftery. 2007 · 2007
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Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht. 2015 · 2015
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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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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. 2017 · 2017
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Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J Liu, and Christopher D Manning. 2017 · 2017
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Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization
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Abstractive summarization of Reddit posts with multi-level memory networks
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When does label smoothing help?
Rafael Müller, Simon Kornblith, and Geoffrey E Hinton. 2019 · 2019
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Improving back-translation with uncertainty-based confidence estimation
Shuo Wang, Yang Liu, Chao Wang, Huanbo Luan, and Maosong Sun. 2019 · 2019
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Unsupervised quality estimation for neural machine translation
Marina Fomicheva, Shuo Sun, Lisa Yankovskaya, Frédéric Blain, Francisco Guzmán, Mark Fishel, Nikolaos Aletras, Vishrav Chaudhary, and Lucia Specia. 2020 · 2020
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Simple and principled uncertainty estimation with deterministic deep learning via distance awareness
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Uncertainty estimation in autoregressive structured prediction
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Uncertainty baselines: Benchmarks for uncertainty & robustness in deep learning
Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael W Dusenberry, Sebastian Farquhar, Qixuan Feng, Angelos Filos, Marton Havasi, Rodolphe Jenatton, et al. 2021 · 2021
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Uncertainty quantification and deep ensembles
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Alleviating exposure bias via contrastive learning for abstractive text summarization
Shichao Sun and Wenjie Li. 2021 · 2021
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Should we trust this summary? bayesian abstractive summarization to the rescue
Alexios Gidiotis and Grigorios Tsoumakas. 2022 · 2022
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On the inference calibration of neural machine translation
Shuo Wang, Zhaopeng Tu, Shuming Shi, and Yang Liu. 2020 · 2020
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Understanding neural abstractive summarization models via uncertainty
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Cliff: Contrastive learning for improving faithfulness and factuality in abstractive summarization
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Moment multicalibration for uncertainty estimation
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Neural-symbolic inference for robust autoregressive graph parsing via compositional uncertainty quantification
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Brio: Bringing order to abstractive summarization
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Out-of-distribution detection and selective generation for conditional language models
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Plex: Towards reliability using pretrained large model extensions
Dustin Tran, Jeremiah Liu, Michael W Dusenberry, Du Phan, Mark Collier, Jie Ren, Kehang Han, Zi Wang, Zelda Mariet, Huiyi Hu, et al. 2022 · 2022
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Sequence level contrastive learning for text summarization
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Calibrating sequence likelihood improves conditional language generation
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