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Neural machine translation systems have become state-of-the-art approaches for Grammatical Error Correction (GEC) task.
A method for solving the convex programming problem with convergence rate o (1/kˆ 2)
Yurii E Nesterov. 1983 · 1983
Earlier work this paper cites.
The cambridge learner corpus: Error coding and analysis for lexicography and elt
Diane Nicholls. 2003 · 2003
Earlier work this paper cites.
A unified architecture for natural language processing: Deep neural networks with multitask learning
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Earlier work this paper cites.
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A new dataset and method for automatically grading esol texts
Helen Yannakoudakis, Ted Briscoe, and Ben Medlock. 2011 · 2011
Earlier work this paper cites.
Better evaluation for grammatical error correction
Daniel Dahlmeier and Hwee Tou Ng. 2012 · 2012
Earlier work this paper cites.
Tense and aspect error correction for esl learners using global context
Toshikazu Tajiri, Mamoru Komachi, and Yuji Matsumoto. 2012 · 2012
Earlier work this paper cites.
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
Earlier work this paper cites.
Building a large annotated corpus of learner english: The nus corpus of learner english
Daniel Dahlmeier, Hwee Tou Ng, and Siew Mei Wu. 2013 · 2013
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
Earlier work this paper cites.
Grammatical error correction using hybrid systems and type filtering
Mariano Felice, Zheng Yuan, Øistein E Andersen, Helen Yannakoudakis, and Ekaterina Kochmar. 2014 · 2014
Earlier work this paper cites.
The amu system in the conll-2014 shared task: Grammatical error correction by data-intensive and feature-rich statistical machine translation
Marcin Junczys-Dowmunt and Roman Grundkiewicz. 2014 · 2014
Earlier work this paper cites.
The conll-2014 shared task on grammatical error correction
Hwee Tou Ng, Siew Mei Wu, Ted Briscoe, Christian Hadiwinoto, Raymond Hendy Susanto, and Christopher Bryant. 2014 · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
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Facial landmark detection by deep multi-task learning
Zhanpeng Zhang, Ping Luo, Chen Change Loy, and Xiaoou Tang. 2014 · 2014
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Instance-aware semantic segmentation via multi-task network cascades
Jifeng Dai, Kaiming He, and Jian Sun. 2016 · 2016
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Incorporating copying mechanism in sequence-to-sequence learning
Jiatao Gu, Zhengdong Lu, Hang Li, and Victor OK Li. 2016 · 2016
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Data recombination for neural semantic parsing
Robin Jia and Percy Liang. 2016 · 2016
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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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Jfleg: A fluency corpus and benchmark for grammatical error correction
Courtney Napoles, Keisuke Sakaguchi, and Joel Tetreault. 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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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Neural sequence-labelling models for grammatical error correction
Helen Yannakoudakis, Marek Rei, Øistein E Andersen, and Zheng Yuan. 2017 · 2017
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Marcin Junczys-Dowmunt and Roman Grundkiewicz. 2016 · 2016
Cited alongside, same era.
Unsupervised pretraining for sequence to sequence learning
Prajit Ramachandran, Peter J Liu, and Quoc V Le. 2016 · 2016
Cited alongside, same era.
Grammatical error correction: Machine translation and classifiers
Alla Rozovskaya and Dan Roth. 2016 · 2016
Cited alongside, same era.
Reassessing the goals of grammatical error correction: Fluency instead of grammaticality
Keisuke Sakaguchi, Courtney Napoles, Matt Post, and Joel Tetreault. 2016 · 2016
Cited alongside, same era.
Deep multi-task learning with low level tasks supervised at lower layers
Anders Søgaard and Yoav Goldberg. 2016 · 2016
Cited alongside, same era.
Deep architectures for neural machine translation
Antonio Valerio Miceli Barone, Jindřich Helcl, Rico Sennrich, Barry Haddow, and Alexandra Birch. 2017 · 2017
Cited alongside, same era.
Convolutional sequence to sequence learning
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N Dauphin. 2017 · 2017
Cited alongside, same era.
Shamil Chollampatt and Hwee Tou Ng. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Reaching human-level performance in automatic grammatical error correction: An empirical study
Tao Ge, Furu Wei, and Ming Zhou. 2018 · 2018
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Near human-level performance in grammatical error correction with hybrid machine translation
Roman Grundkiewicz and Marcin Junczys-Dowmunt. 2018 · 2018
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Approaching neural grammatical error correction as a low-resource machine translation task
Marcin Junczys-Dowmunt, Roman Grundkiewicz, Shubha Guha, and Kenneth Heafield. 2018 · 2018
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Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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An analysis of encoder representations in transformer-based machine translation
Alessandro Raganato, Jörg Tiedemann, et al. 2018 · 2018
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