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Neural models have revolutionized the field of machine translation, but creating parallel corpora is expensive and time-consuming.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
SwitchOut: an efficient data augmentation algorithm for neural machine translation
Xinyi Wang, Hieu Pham, Zihang Dai, and Graham Neubig. 2018 · 2018
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Generalized data augmentation for low-resource translation
Mengzhou Xia, Xiang Kong, Antonios Anastasopoulos, and Graham Neubig. 2019 · 2019
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared 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 M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher 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 · 2020
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An analysis of massively multilingual neural machine translation for low-resource languages
Aaron Mueller, Garrett Nicolai, Arya D. McCarthy, Dylan Lewis, Winston Wu, and David Yarowsky. 2020 · 2020
Earlier work this paper cites.
SSMBA: Self-supervised manifold based data augmentation for improving out-of-domain robustness
Nathan Ng, Kyunghyun Cho, and Marzyeh Ghassemi. 2020 · 2020
Cited alongside, same era.
Dictionary-based data augmentation for cross-domain neural machine translation
Wei Peng, Chongxuan Huang, Tianhao Li, Yun Chen, and Qun Liu. 2020 · 2020
Cited alongside, same era.
Making monolingual sentence embeddings multilingual using knowledge distillation
Nils Reimers and Iryna Gurevych. 2020 · 2020
Cited alongside, same era.
More than just frequency? demasking unsupervised hypernymy prediction methods
Thomas Bott, Dominik Schlechtweg, and Sabine Schulte im Walde. 2021 · 2021
Cited alongside, same era.
A survey of data augmentation approaches for NLP
Steven Y. Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, and Eduard Hovy. 2021 · 2021
Cited alongside, same era.
Adapting high-resource NMT models to translate low-resource related languages without parallel data
Wei-Jen Ko, Ahmed El-Kishky, Adithya Renduchintala, Vishrav Chaudhary, Naman Goyal, Francisco Guzmán, Pascale Fung, Philipp Koehn, and Mona Diab. 2021 · 2021
Later among the works it cites.
The curious case of hallucinations in neural machine translation
Vikas Raunak, Arul Menezes, and Marcin Junczys-Dowmunt. 2021 · 2021
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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.
Is chatgpt a good translator? yes with gpt-4 as the engine
Wenxiang Jiao, Wenxuan Wang, Jen tse Huang, Xing Wang, and Zhaopeng Tu. 2023 · 2023
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Improving neural machine translation models with monolingual data
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016b
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016c
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