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Large-scale pre-trained language models such as GPT-3 have shown remarkable performance across various natural language processing tasks.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 1901
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The computer learner corpus: a versatile new source of data for sla research
Sylviane Granger. 1998 · 1998
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A new dataset and method for automatically grading ESOL texts
Helen Yannakoudakis, Ted Briscoe, and Ben Medlock. 2011 · 2011
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The effect of learner corpus size in grammatical error correction of ESL writings
Tomoya Mizumoto, Yuta Hayashibe, Mamoru Komachi, Masaaki Nagata, and Yuji Matsumoto. 2012 · 2012
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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
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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
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Grammatical error correction using neural machine translation
Zheng Yuan and Ted Briscoe. 2016 · 2016
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A nested attention neural hybrid model for grammatical error correction
Jianshu Ji, Qinlong Wang, Kristina Toutanova, Yongen Gong, Steven Truong, and Jianfeng Gao. 2017 · 2017
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Google’s multilingual neural machine translation system: Enabling zero-shot translation
Melvin Johnson, Mike Schuster, Quoc V. Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernanda Viégas, Martin Wattenberg, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 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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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction
Shamil Chollampatt and Hwee Tou Ng. 2018 · 2018
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Fluency boost learning and inference for neural grammatical error correction
Tao Ge, Furu Wei, and Ming Zhou. 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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Noising and denoising natural language: Diverse backtranslation for grammar correction
Ziang Xie, Guillaume Genthial, Stanley Xie, Andrew Ng, and Dan Jurafsky. 2018 · 2018
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The unreasonable effectiveness of transformer language models in grammatical error correction
Dimitris Alikaniotis and Vipul Raheja. 2019 · 2019
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The BEA-2019 shared task on grammatical error correction
Christopher Bryant, Mariano Felice, Øistein E. Andersen, and Ted Briscoe. 2019 · 2019
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A neural grammatical error correction system built on better pre-training and sequential transfer learning
Yo Joong Choe, Jiyeon Ham, Kyubyong Park, and Yeoil Yoon. 2019 · 2019
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Noisy channel for low resource grammatical error correction
Simon Flachs, Ophélie Lacroix, and Anders Søgaard. 2019 · 2019
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Minimally-augmented grammatical error correction
Roman Grundkiewicz and Marcin Junczys-Dowmunt. 2019 · 2019
Cited alongside, same era.
Neural grammatical error correction systems with unsupervised pre-training on synthetic data
Roman Grundkiewicz, Marcin Junczys-Dowmunt, and Kenneth Heafield. 2019 · 2019
Cited alongside, same era.
Controlling grammatical error correction using word edit rate
Improving grammatical error correction models with purpose-built adversarial examples
Lihao Wang and Xiaoqing Zheng. 2020 · 2020
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Cross-lingual transfer learning for grammatical error correction
Ikumi Yamashita, Satoru Katsumata, Masahiro Kaneko, Aizhan Imankulova, and Mamoru Komachi. 2020 · 2020
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SOME: Reference-less sub-metrics optimized for manual evaluations of grammatical error correction
Ryoma Yoshimura, Masahiro Kaneko, Tomoyuki Kajiwara, and Mamoru Komachi. 2020 · 2020
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Improving grammatical error correction with machine translation pairs
Wangchunshu Zhou, Tao Ge, Chang Mu, Ke Xu, Furu Wei, and Ming Zhou. 2020 · 2020
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Comparison of grammatical error correction using back-translation models
Aomi Koyama, Kengo Hotate, Masahiro Kaneko, and Mamoru Komachi. 2021a · 2021
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Kengo Hotate, Masahiro Kaneko, Satoru Katsumata, and Mamoru Komachi. 2019 · 2019
Cited alongside, same era.
An empirical study of incorporating pseudo data into grammatical error correction
Shun Kiyono, Jun Suzuki, Masato Mita, Tomoya Mizumoto, and Kentaro Inui. 2019 · 2019
Cited alongside, same era.
Corpora generation for grammatical error correction
Jared Lichtarge, Chris Alberti, Shankar Kumar, Noam Shazeer, Niki Parmar, and Simon Tong. 2019 · 2019
Cited alongside, same era.
Improving precision of grammatical error correction with a cheat sheet
Mengyang Qiu, Xuejiao Chen, Maggie Liu, Krishna Parvathala, Apurva Patil, and Jungyeul Park. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Cited alongside, same era.
Erroneous data generation for grammatical error correction
Shuyao Xu, Jiehao Zhang, Jin Chen, and Long Qin. 2019 · 2019
Cited alongside, same era.
Improving grammatical error correction via pre-training a copy-augmented architecture with unlabeled data
Wei Zhao, Liang Wang, Kewei Shen, Ruoyu Jia, and Jingming Liu. 2019 · 2019
Cited alongside, same era.
Synthetic data with neural machine translation for automatic correction in arabic grammar
Aiman Solyman, Wang Zhenyu, Tao Qian, Arafat Abdulgader Mohammed Elhag, Muhammad Toseef, and Zeinab Aleibeid. 2021 · 2021
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LM-critic: Language models for unsupervised grammatical error correction
Michihiro Yasunaga, Jure Leskovec, and Percy Liang. 2021 · 2021
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Editeval: An instruction-based benchmark for text improvements
Jane Dwivedi-Yu, Timo Schick, Zhengbao Jiang, Maria Lomeli, Patrick Lewis, Gautier Izacard, Edouard Grave, Sebastian Riedel, and Fabio Petroni. 2022 · 2022
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Interpretability for language learners using example-based grammatical error correction
Masahiro Kaneko, Sho Takase, Ayana Niwa, and Naoaki Okazaki. 2022 · 2022
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IMPARA: Impact-based metric for GEC using parallel data
Koki Maeda, Masahiro Kaneko, and Naoaki Okazaki. 2022 · 2022
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Peer: A collaborative language model
Timo Schick, Jane Dwivedi-Yu, Zhengbao Jiang, Fabio Petroni, Patrick Lewis, Gautier Izacard, Qingfei You, Christoforos Nalmpantis, Edouard Grave, and Sebastian Riedel. 2022 · 2022
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ProQE: Proficiency-wise quality estimation dataset for grammatical error correction
Yujin Takahashi, Masahiro Kaneko, Masato Mita, and Mamoru Komachi. 2022 · 2022
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Do prompt-based models really understand the meaning of their prompts?
Albert Webson and Ellie Pavlick. 2022 · 2022
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An analysis of gpt-3’s performance in grammatical error correction
Steven Coyne and Keisuke Sakaguchi. 2023 · 2023
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Is chatgpt a highly fluent grammatical error correction system? a comprehensive evaluation
Tao Fang, Shu Yang, Kaixin Lan, Derek F. Wong, Jinpeng Hu, Lidia S. Chao, and Yue Zhang. 2023 · 2023
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