Fetching the paper…
Reading the bibliography…
GPT-3 and GPT-4 models are powerful, achieving high performance on a variety of 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, et al. 2020 · 1901
Earlier work this paper cites.
Frustratingly easy system combination for grammatical error correction
Muhammad Qorib, Seung-Hoon Na, and Hwee Tou Ng. 2022 · 1974
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.
Ground truth for grammatical error correction metrics
Courtney Napoles, Keisuke Sakaguchi, Matt Post, and Joel Tetreault. 2015 · 2015
Earlier work this paper cites.
JFLEG: A fluency corpus and benchmark for grammatical error correction
Courtney Napoles, Keisuke Sakaguchi, and Joel Tetreault. 2017 · 2017
Earlier work this paper cites.
A multilayer convolutional encoder-decoder neural network for grammatical error correction
Shamil Chollampatt and Hwee Tou Ng. 2018 · 2018
Earlier work this paper cites.
Near human-level performance in grammatical error correction with hybrid machine translation
Roman Grundkiewicz and Marcin Junczys-Dowmunt. 2018 · 2018
Earlier work this paper cites.
Approaching neural grammatical error correction as a low-resource machine translation task
Marcin Junczys-Dowmunt, Roman Grundkiewicz, Shubha Guha, and Kenneth Heafield. 2018 · 2018
Earlier work this paper cites.
Efficient online scalar annotation with bounded support
Keisuke Sakaguchi and Benjamin Van Durme. 2018 · 2018
Earlier work this paper cites.
The BEA-2019 shared task on grammatical error correction
Christopher Bryant, Mariano Felice, Øistein E. Andersen, and Ted Briscoe. 2019 · 2019
Earlier work this paper cites.
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.
Use your mind and learn to write: The problem of producing coherent text
Michael Zock and Debela Tesfaye Gemechu. 2017 · 2019
Cited alongside, same era.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020 · 2020
Cited alongside, same era.
How can we know what language models know?
Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig. 2020 · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Synthetic data generation for grammatical error correction with tagged corruption models
Felix Stahlberg and Shankar Kumar. 2021 · 2021
Later among the works it cites.
LM-critic: Language models for unsupervised grammatical error correction
Michihiro Yasunaga, Jure Leskovec, and Percy Liang. 2021 · 2021
Later among the works it cites.
Really good grammatical error correction, and how to evaluate it
Robert Ostling and Murathan Kurfalı. 2022 · 2022
Later among the works it cites.
Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. 2022 · 2022
Later among the works it cites.
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
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
GECToR – grammatical error correction: Tag, not rewrite
Kostiantyn Omelianchuk, Vitaliy Atrasevych, Artem Chernodub, and Oleksandr Skurzhanskyi. 2020 · 2020
Cited alongside, same era.
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2020
Cited alongside, same era.
Is this the end of the gold standard? a straightforward reference-less grammatical error correction metric
Md Asadul Islam and Enrico Magnani. 2021 · 2021
Cited alongside, same era.
Neural quality estimation with multiple hypotheses for grammatical error correction
Zhenghao Liu, Xiaoyuan Yi, Maosong Sun, Liner Yang, and Tat-Seng Chua. 2021 · 2021
Cited alongside, same era.
It’s not just size that matters: Small language models are also few-shot learners
Timo Schick and Hinrich Schütze. 2021 · 2021
Cited alongside, same era.
Later among the works it cites.
Ensembling and knowledge distilling of large sequence taggers for grammatical error correction
Maksym Tarnavskyi, Artem Chernodub, and Kostiantyn Omelianchuk. 2022 · 2022
Later among the works it cites.
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
Closest in time.
OpenAI. 2023 · 2023
Closest in time.
Chatgpt or grammarly? evaluating chatgpt on grammatical error correction benchmark
Haoran Wu, Wenxuan Wang, Yuxuan Wan, Wenxiang Jiao, and Michael Lyu. 2023 · 2023
Closest in time.