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Despite the reported success of unsupervised machine translation (MT), the field has yet to examine the conditions under which these methods succeed, and where they fail.
On the robustness of unsupervised and semi-supervised cross-lingual word embedding learning
Yerai Doval, Jose Camacho-Collados, Luis Espinosa-Anke, and Steven Schockaert. 2019 · 1908
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
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Compiling bilingual lexicon entries from a non-parallel English-Chinese corpus
Pascale Fung. 1995 · 1995
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Identifying word translations in non-parallel texts
Reinhard Rapp. 1995 · 1995
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Estimating word translation probabilities from unrelated monolingual corpora using the em algorithm
Philipp Koehn and Kevin Knight. 2000 · 2000
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Multilingual denoising pre-training for neural machine translation
Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, and Luke Zettlemoyer. 2020 · 2001
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Learning a translation lexicon from monolingual corpora
Philipp Koehn and Kevin Knight. 2002 · 2002
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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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Minimum error rate training in statistical machine translation
Franz Josef Och. 2003 · 2003
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When and why is unsupervised neural machine translation useless?
Y. Kim, M. Graça, and H. Ney. 2020 · 2004
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Moses: Open source toolkit for statistical machine translation
Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Chris Dyer, Ondřej Bojar, Alexandra Constantin, and Evan Herbst. 2007 · 2007
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Learning bilingual lexicons from monolingual corpora
Aria Haghighi, Percy Liang, Taylor Berg-Kirkpatrick, and Dan Klein. 2008 · 2008
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KenLM: Faster and smaller language model queries
Kenneth Heafield. 2011 · 2011
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Deciphering foreign language
Sujith Ravi and Kevin Knight. 2011 · 2011
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Parallel data, tools and interfaces in OPUS
Jörg Tiedemann. 2012 · 2012
Cited alongside, same era.
Recurrent continuous translation models
Nal Kalchbrenner and Phil Blunsom. 2013 · 2013
Cited alongside, same era.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
Cited alongside, same era.
Building subject-aligned comparable corpora and mining it for truly parallel sentence pairs
Krzysztof Wołk and Krzysztof Marasek. 2014 · 2014
Cited alongside, same era.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
Cited alongside, same era.
A call for clarity in reporting BLEU scores
Matt Post. 2018 · 2018
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On the limitations of unsupervised bilingual dictionary induction
Anders Søgaard, Sebastian Ruder, and Ivan Vulić. 2018 · 2018
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Unsupervised neural machine translation with weight sharing
Zhen Yang, Wei Chen, Feng Wang, and Bo Xu. 2018 · 2018
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An effective approach to unsupervised machine translation
Mikel Artetxe, Gorka Labaka, and Eneko Agirre. 2019 · 2019
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Cross-lingual language model pretraining
Alexis Conneau and Guillaume Lample. 2019 · 2019
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How to (properly) evaluate cross-lingual word embeddings: On strong baselines, comparative analyses, and some misconceptions
Goran Glavaš, Robert Litschko, Sebastian Ruder, and Ivan Vulić. 2019 · 2019
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Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Cited alongside, same era.
The united nations parallel corpus v1.0
Michał Ziemski, Marcin Junczys-Dowmunt, and Bruno Pouliquen. 2016 · 2016
Cited alongside, same era.
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
Cited alongside, same era.
Gromov-Wasserstein alignment of word embedding spaces
David Alvarez-Melis and Tommi Jaakkola. 2018 · 2018
Cited alongside, same era.
Unsupervised statistical machine translation
Mikel Artetxe, Gorka Labaka, and Eneko Agirre. 2018b · 2018
Cited alongside, same era.
Word translation without parallel data
Alexis Conneau, Guillaume Lample, Marc’Aurelio Ranzato, Ludovic Denoyer, and Hervé Jégou. 2018 · 2018
Cited alongside, same era.
Non-adversarial unsupervised word translation
Yedid Hoshen and Lior Wolf. 2018 · 2018
Cited alongside, same era.
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The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English
Francisco Guzmán, Peng-Jen Chen, Myle Ott, Juan Pino, Guillaume Lample, Philipp Koehn, Vishrav Chaudhary, and Marc’Aurelio Ranzato. 2019 · 2019
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Comparing unsupervised word translation methods step by step
Mareike Hartmann, Yova Kementchedjhieva, and Anders Søgaard. 2019 · 2019
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Analyzing the limitations of cross-lingual word embedding mappings
Aitor Ormazabal, Mikel Artetxe, Gorka Labaka, Aitor Soroa, and Eneko Agirre. 2019 · 2019
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fairseq: A fast, extensible toolkit for sequence modeling
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019 · 2019
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Bilingual lexicon induction with semi-supervision in non-isometric embedding spaces
Barun Patra, Joel Ruben Antony Moniz, Sarthak Garg, Matthew R. Gormley, and Graham Neubig. 2019 · 2019
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A survey of cross-lingual word embedding models
Sebastian Ruder, Ivan Vulić, and Anders Søgaard. 2019 · 2019
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Mass: Masked sequence to sequence pre-training for language generation
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. 2019 · 2019
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Do we really need fully unsupervised cross-lingual embeddings?
Ivan Vulić, Goran Glavaš, Roi Reichart, and Anna Korhonen. 2019 · 2019
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