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It has been shown that the performance of neural machine translation (NMT) drops starkly in low-resource conditions, often requiring large amounts of auxiliary data to achieve competitive results.
Cross-lingual language model pretraining
Guillaume Lample and Alexis Conneau. 2019 · 1901
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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Statistical phrase-based translation
Philipp Koehn, Franz Josef Och, and Daniel Marcu. 2003 · 2003
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Yemane Keleta, Kazuhide Yamamoto, and Ashuboda Marasinghe. 2016 · 2016
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Jiajun Zhang and Chengqing Zong. 2016 · 2016
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Transfer learning for low-resource neural machine translation
Barret Zoph, Deniz Yuret, Jonathan May, and Kevin Knight. 2016 · 2016
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The similarity and mutual intelligibility between Amharic and Tigrigna varieties
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Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senellart, and Alexander Rush. 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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Iterative back-translation for neural machine translation
Parallel corpora for bi-directional statistical machine translation for seven Ethiopian language pairs
Solomon Teferra Abate, Michael Melese, Martha Yifiru Tachbelie, Million Meshesha, Solomon Atinafu, Wondwossen Mulugeta, Yaregal Assabie, Hafte Abera, Binyam Ephrem, Tewodros Abebe, Wondimagegnhue Tsegaye, Amanuel Lemma, Tsegaye Andargie, and Seifedin Shifaw. 2018 · 2018
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compare-mt: A tool for holistic comparison of language generation systems
Graham Neubig, Zi-Yi Dou, Junjie Hu, Paul Michel, Danish Pruthi, and Xinyi Wang. 2019 · 2019
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Facebook FAIR’s WMT19 news translation task submission
Nathan Ng, Kyra Yee, Alexei Baevski, Myle Ott, Michael Auli, and Sergey Edunov. 2019 · 2019
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Revisiting low-resource neural machine translation: A case study
Rico Sennrich and Biao Zhang. 2019 · 2019
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Tangled up in BLEU: Reevaluating the evaluation of automatic machine translation evaluation metrics
Nitika Mathur, Timothy Baldwin, and Trevor Cohn. 2020 · 2020
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Vu Cong Duy Hoang, Philipp Koehn, Gholamreza Haffari, and Trevor Cohn. 2018 · 2018
Cited alongside, same era.
NICT self-training approach to neural machine translation at NMT-2018
Kenji Imamura and Eiichiro Sumita. 2018 · 2018
Cited alongside, same era.
Phrase-based & neural unsupervised machine translation
Guillaume Lample, Myle Ott, Alexis Conneau, Ludovic Denoyer, and Marc’Aurelio Ranzato. 2018d · 2018
Cited alongside, same era.
Unsupervised machine translation using monolingual corpora only
Guillaume Lample, Alexis Conneau, Ludovic Denoyer, and Marc’Aurelio Ranzato. 2018a
Cited in the paper.
Word translation without parallel data
Guillaume Lample, Alexis Conneau, Marc’Aurelio Ranzato, Ludovic Denoyer, and Hervé Jégou. 2018b
Cited in the paper.
Phrase-based & neural unsupervised machine translation
Guillaume Lample, Myle Ott, Alexis Conneau, Ludovic Denoyer, and Marc’Aurelio Ranzato. 2018c
Cited in the paper.
Improving neural machine translation models with monolingual data
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016a
Cited in the paper.
BLEURT: Learning robust metrics for text generation
Thibault Sellam, Dipanjan Das, and Ankur Parikh. 2020 · 2020
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W3techs. usage of content languages for websites
W3Techs. 2020 · 2020
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Bertscore: Evaluating text generation with bert
Tianyi Zhang*, Varsha Kishore*, Felix Wu*, Kilian Q. Weinberger, and Yoav Artzi. 2020 · 2020
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