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This paper investigates how to effectively incorporate a pre-trained masked language model (MLM), such as BERT, into an encoder-decoder (EncDec) model for grammatical error correction (GEC).
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Earlier work this paper cites.
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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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Ground Truth for Grammatical Error Correction Metrics
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Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna. 2016 · 2016
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Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction
Christopher Bryant, Mariano Felice, and Ted Briscoe. 2017 · 2017
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Grammatical Error Detection Using Error- and Grammaticality-Specific Word Embeddings
Masahiro Kaneko, Yuya Sakaizawa, and Mamoru Komachi. 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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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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Developing an Automated Writing Placement System for ESL Learners
Helen Yannakoudakis, Ãistein E. Andersen, Geranpayeh Ardeshir, Briscoe Ted, and Nicholls Diane. 2018 · 2018
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The AIP-Tohoku System at the BEA-2019 Shared Task
Hiroki Asano, Masato Mita, Tomoya Mizumoto, and Jun Suzuki. 2019 · 2019
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Parallel Iterative Edit Models for Local Sequence Transduction
Learning to combine Grammatical Error Corrections
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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Cross-lingual Language Model Pretraining
Guillaume Lample and Alexis Conneau. 2019 · 2019
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Corpora Generation for Grammatical Error Correction
Jared Lichtarge, Chris Alberti, Shankar Kumar, Noam Shazeer, Niki Parmar, and Simon Tong. 2019 · 2019
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Fine-tune BERT for Extractive Summarization
Yang Liu. 2019 · 2019
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Cross-Corpora Evaluation and Analysis of Grammatical Error Correction Models — Is Single-Corpus Evaluation Enough?
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Abhijeet Awasthi, Sunita Sarawagi, Rasna Goyal, Sabyasachi Ghosh, and Vihari Piratla. 2019 · 2019
Cited alongside, same era.
Context is Key: Grammatical Error Detection with Contextual Word Representations
Samuel Bell, Helen Yannakoudakis, and Marek Rei. 2019 · 2019
Cited alongside, same era.
The BEA-2019 Shared Task on Grammatical Error Correction
Christopher Bryant, Mariano Felice, Øistein E. Andersen, and Ted Briscoe. 2019 · 2019
Cited alongside, same era.
Cross-Sentence Grammatical Error Correction
Shamil Chollampatt, Weiqi Wang, and Hwee Tou Ng. 2019 · 2019
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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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Neural Grammatical Error Correction Systems with Unsupervised Pre-training on Synthetic Data
Roman Grundkiewicz, Marcin Junczys-Dowmunt, and Kenneth Heafield. 2019 · 2019
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TMU Transformer System Using BERT for Re-ranking at BEA 2019 Grammatical Error Correction on Restricted Track
Masahiro Kaneko, Kengo Hotate, Satoru Katsumata, and Mamoru Komachi. 2019 · 2019
Cited alongside, same era.
Masato Mita, Tomoya Mizumoto, Masahiro Kaneko, Ryo Nagata, and Kentaro Inui. 2019 · 2019
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Leveraging pre-trained checkpoints for sequence generation tasks
Sascha Rothe, Shashi Narayan, and Aliaksei Severyn. 2019 · 2019
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Acquiring Knowledge from Pre-trained Model to Neural Machine Translation
Rongxiang Weng, Heng Yu, Shujian Huang, Shanbo Cheng, and Weihua Luo. 2019 · 2019
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HuggingFace’s Transformers: State-of-the-art Natural Language Processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, R’emi Louf, Morgan Funtowicz, and Jamie Brew. 2019 · 2019
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Pretraining-Based Natural Language Generation for Text Summarization
Haoyu Zhang, Yeyun Gong, Yu Yan, Nan Duan, Jianjun Xu, Ji Wang, Ming Gong, and Ming Zhou. 2019 · 2019
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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
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Pre-trained Models for Natural Language Processing: A Survey
Xipeng Qiu, Tianxiang Sun, Yige Xu, Yunfan Shao, Ning Dai, and Xuanjing Huang. 2020 · 2020
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Incorporating BERT into Neural Machine Translation
Jinhua Zhu, Yingce Xia, Lijun Wu, Di He, Tao Qin, Wengang Zhou, Houqiang Li, and Tieyan Liu. 2020 · 2020
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