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We release a corpus of 43 million atomic edits across 8 languages.
Linguistics and natural logic
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Extracting context-rich entailment rules from wikipedia revision history
Elena Cabrio, Bernardo Magnini, and Angelina Ivanova. 2012 · 2012
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A corpus-based study of edit categories in featured and non-featured Wikipedia articles
Johannes Daxenberger and Iryna Gurevych. 2012 · 2012
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Japanese and korean voice search
Mike Schuster and Kaisuke Nakajima. 2012 · 2012
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Measuring contextual fitness using error contexts extracted from the wikipedia revision history
Torsten Zesch. 2012 · 2012
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Robust systems for preposition error correction using wikipedia revisions
Aoife Cahill, Nitin Madnani, Joel Tetreault, and Diane Napolitano. 2013 · 2013
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One billion word benchmark for measuring progress in statistical language modeling
Ciprian Chelba, Tomas Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, Phillipp Koehn, and Tony Robinson. 2013 · 2013
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Globally normalized transition-based neural networks
Daniel Andor, Chris Alberti, David Weiss, Aliaksei Severyn, Alessandro Presta, Kuzman Ganchev, Slav Petrov, and Michael Collins. 2016 · 2016
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Exploring the limits of language modeling
Rafal Jozefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, and Yonghui Wu. 2016 · 2016
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Supervised learning of universal sentence representations from natural language inference data
Alexis Conneau, Douwe Kiela, Holger Schwenk, Loic Barrault, and Antoine Bordes. 2017 · 2017
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Discourse-based objectives for fast unsupervised sentence representation learning
Yacine Jernite, Samuel R Bowman, and David Sontag. 2017 · 2017
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Learned in translation: Contextualized word vectors
Bryan McCann, James Bradbury, Caiming Xiong, and Richard Socher. 2017 · 2017
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Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
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Chenhao Tan and Lillian Lee. 2014 · 2014
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Skip-thought vectors
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Split and rephrase
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Dissent: Sentence representation learning from explicit discourse relations
Allen Nie, Erin D Bennett, and Noah D Goodman. 2017 · 2017
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Identifying semantic edit intentions from revisions in wikipedia
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Learning word vectors for 157 languages
Edouard Grave, Piotr Bojanowski, Prakhar Gupta, Armand Joulin, and Tomas Mikolov. 2018 · 2018
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Generating sentences by editing prototypes
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Advances in pre-training distributed word representations
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Deep contextualized word representations
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