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Autoregressive models for text sometimes generate repetitive and low-quality output because errors accumulate during the steps of generation.
A learning algorithm for continually running fully recurrent neural networks
Ronald J Williams and David Zipser · 1989
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
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Towards a general rule for identifying deceptive opinion spam
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Scheduled sampling for sequence prediction with recurrent neural networks
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Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom · 2015
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Deep variational information bottleneck
Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy · 2016
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Professor forcing: A new algorithm for training recurrent networks
Alex M Lamb, Anirudh Goyal ALIAS PARTH GOYAL, Ying Zhang, Saizheng Zhang, Aaron C Courville, and Yoshua Bengio · 2016
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Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu · 2017
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A tutorial on deep latent variable models of natural language
Yoon Kim, Sam Wiseman, and Alexander M Rush · 2018
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Analyzing uncertainty in neural machine translation
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A call for clarity in reporting BLEU scores
Matt Post · 2018
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Ting Chen, Ruixiang Zhang, and Geoffrey Hinton · 2022
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Subspace diffusion generative models
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Diffusion-lm improves controllable text generation
Xiang Li, John Thickstun, Ishaan Gulrajani, Percy S Liang, and Tatsunori B Hashimoto · 2022
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Genie: Large scale pre-training for text generation with diffusion model
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Generative modeling by estimating gradients of the data distribution
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Diffuseq: Sequence to sequence text generation with diffusion models
Shansan Gong, Mukai Li, Jiangtao Feng, Zhiyong Wu, and LingPeng Kong · 2023
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