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Neural language models are usually trained to match the distributional properties of a large-scale corpus by minimizing the log loss.
Evaluating the state-of-the-art of end-to-end natural language generation: The E2E NLG Challenge
Ondřej Dušek, Jekaterina Novikova, and Verena Rieser. 2019 · 1901
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The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Maxwell Forbes, and Yejin Choi. 2019 · 1904
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Defending against neural fake news
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Constrained decoding for neural nlg from compositional representations in task-oriented dialogue
Anusha Balakrishnan, Jinfeng Rao, Kartikeya Upasani, Michael White, and Rajen Subba. 2019 · 1906
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Handling divergent reference texts when evaluating table-to-text generation
Bhuwan Dhingra, Manaal Faruqui, Ankur Parikh, Ming-Wei Chang, Dipanjan Das, and William W Cohen. 2019 · 1906
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Gltr: Statistical detection and visualization of generated text
Sebastian Gehrmann, Hendrik Strobelt, and Alexander M Rush. 2019 · 1906
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Neural text summarization: A critical evaluation
Wojciech Kryściński, Nitish Shirish Keskar, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 1908
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Neural text generation with unlikelihood training
Sean Welleck, Ilia Kulikov, Stephen Roller, Emily Dinan, Kyunghyun Cho, and Jason Weston. 2019 · 1908
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Ctrl: A conditional transformer language model for controllable generation
Nitish Shirish Keskar, Bryan McCann, Lav R Varshney, Caiming Xiong, and Richard Socher. 2019 · 1909
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Sticking to the facts: Confident decoding for faithful data-to-text generation
Ran Tian, Shashi Narayan, Thibault Sellam, and Ankur P Parikh. 2019 · 1910
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A survey of sampling from contaminated distributions
John W Tukey. 1960 · 1960
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Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
Martin A Fischler and Robert C Bolles. 1981 · 1981
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Breakdown properties of multivariate location estimators
DL Donoho. 1982 · 1982
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The” automatic” robustness of minimum distance functionals
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Robust estimation of a location parameter
Peter J Huber. 1992 · 1992
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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Automatic evaluation of summaries using n-gram co-occurrence statistics
Chin-Yew Lin and Eduard Hovy. 2003 · 2003
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Minimum error rate training in statistical machine translation
Franz Josef Och. 2003 · 2003
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Word reordering and a dynamic programming beam search algorithm for statistical machine translation
Christoph Tillmann and Hermann Ney. 2003 · 2003
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Sentence-level mt evaluation without reference translations: Beyond language modeling
Michael Gamon, Anthony Aue, and Martine Smets. 2005 · 2005
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Learning with annotation noise
Eyal Beigman and Beata Beigman Klebanov. 2009 · 2009
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Some empirical evidence for annotation noise in a benchmarked dataset
Beata Beigman Klebanov and Eyal Beigman. 2010 · 2010
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Information theory: coding theorems for discrete memoryless systems
Imre Csiszar and János Körner. 2011 · 2011
Why we need new evaluation metrics for nlg
Jekaterina Novikova, Ondřej Dušek, Amanda Cercas Curry, and Verena Rieser. 2017 · 2017
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Adversarial generation of natural language
Sai Rajeswar, Sandeep Subramanian, Francis Dutil, Christopher Pal, and Aaron Courville. 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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Challenges in data-to-document generation
Sam Wiseman, Stuart M Shieber, and Alexander M Rush. 2017 · 2017
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Large scale gan training for high fidelity natural image synthesis
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. 2014 · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
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A neural attention model for sentence summarization
Alexander M Rush, SEAS Harvard, Sumit Chopra, and Jason Weston. 2017 · 2015
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Minimum risk training for neural machine translation
Shiqi Shen, Yong Cheng, Zhongjun He, Wei He, Hua Wu, Maosong Sun, and Yang Liu. 2015 · 2015
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Abstractive text summarization using sequence-to-sequence rnns and beyond
Ramesh Nallapati, Bowen Zhou, Caglar Gulcehre, Bing Xiang, et al. 2016 · 2016
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Andrew Brock, Jeff Donahue, and Karen Simonyan. 2018 · 2018
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Massimo Caccia, Lucas Caccia, William Fedus, Hugo Larochelle, Joelle Pineau, and Laurent Charlin. 2018 · 2018
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Sever: A robust meta-algorithm for stochastic optimization
Ilias Diakonikolas, Gautam Kamath, Daniel M Kane, Jerry Li, Jacob Steinhardt, and Alistair Stewart. 2018 · 2018
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Hierarchical neural story generation
Angela Fan, Mike Lewis, and Yann Dauphin. 2018 · 2018
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Bottom-up abstractive summarization
Sebastian Gehrmann, Yuntian Deng, and Alexander Rush. 2018 · 2018
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Learning to write with cooperative discriminators
Ari Holtzman, Jan Buys, Maxwell Forbes, Antoine Bosselut, David Golub, and Yejin Choi. 2018 · 2018
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Hallucinations in neural machine translation
Katherine Lee, Orhan Firat, Ashish Agarwal, Clara Fannjiang, and David Sussillo. 2018 · 2018
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E2e nlg challenge: Neural models vs. templates
Yevgeniy Puzikov and Iryna Gurevych. 2018 · 2018
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Learning to encode text as human-readable summaries using generative adversarial networks
Yau-Shian Wang and Hung-Yi Lee. 2018 · 2018
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Unifying human and statistical evaluation for natural language generation
Tatsunori B Hashimoto, Hugh Zhang, and Percy Liang. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Hype: A benchmark for human eye perceptual evaluation of generative models
Sharon Zhou, Mitchell Gordon, Ranjay Krishna, Austin Narcomey, Li F Fei-Fei, and Michael Bernstein. 2019 · 2019
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