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In recent years, pre-trained models have been extensively studied, and several downstream tasks have benefited from their utilization.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
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The CoNLL-2013 shared task on grammatical error correction
Hwee Tou Ng, Siew Mei Wu, Yuanbin Wu, Christian Hadiwinoto, and Joel Tetreault. 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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Phrase structure annotation and parsing for learner English
Ryo Nagata and Keisuke Sakaguchi. 2016 · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz 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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Youdao’s winning solution to the NLPCC-2018 task 2 challenge: A neural machine translation approach to Chinese grammatical error correction
Kai Fu, Jun Huang, and Yitao Duan. 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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A sequence to sequence learning for Chinese grammatical error correction
Hongkai Ren, Liner Yang, and Endong Xun. 2018 · 2018
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Overview of the NLPCC 2018 shared task: Grammatical error correction
Yuanyuan Zhao, Nan Jiang, Weiwei Sun, and Xiaojun Wan. 2018 · 2018
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Chinese grammatical error correction using statistical and neural models
Junpei Zhou, Chen Li, Hengyou Liu, Zuyi Bao, Guangwei Xu, and Linlin Li. 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. 2019 · 2019
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Learning to combine grammatical error corrections
RoBERTa: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke S. Zettlemoyer, and Veselin Stoyanov. 2019 · 2019
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ERNIE: Enhanced language representation with informative entities
Zhengyan Zhang, Xu Han, Zhiyuan Liu, Xin Jiang, Maosong Sun, and Qun Liu. 2019 · 2019
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Revisiting pre-trained models for Chinese natural language processing
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Shijin Wang, and Guoping Hu. 2020 · 2020
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Encoder-decoder models can benefit from pre-trained masked language models in grammatical error correction
Masahiro Kaneko, Masato Mita, Shun Kiyono, Jun Suzuki, and Kentaro Inui. 2020 · 2020
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MaskGEC: Improving neural grammatical error correction via dynamic masking
Zewei Zhao and Houfeng Wang. 2020 · 2020
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Yoav Kantor, Yoav Katz, Leshem Choshen, Edo Cohen-Karlik, Naftali Liberman, Assaf Toledo, Amir Menczel, and Noam Slonim. 2019 · 2019
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An empirical study of incorporating pseudo data into grammatical error correction
Shun Kiyono, Jun Suzuki, Masato Mita, Tomoya Mizumoto, and Kentaro Inui. 2019 · 2019
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Jinhua Zhu, Yingce Xia, Lijun Wu, Di He, Tao Qin, Wengang Zhou, Houqiang Li, and Tieyan Liu. 2020 · 2020
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