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Adversarial training has shown impressive success in learning bilingual dictionary without any parallel data by mapping monolingual embeddings to a shared space.
Earth mover’s distance minimization for unsupervised bilingual lexicon induction
Meng Zhang, Yang Liu, Huanbo Luan, and Maosong Sun. 2017b · 1945
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Adversarial training for unsupervised bilingual lexicon induction
Meng Zhang, Yang Liu, Huanbo Luan, and Maosong Sun. 2017a · 1970
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Improving vector space word representations using multilingual correlation
Manaal Faruqui and Chris Dyer. 2014 · 2014
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Improving zero-shot learning by mitigating the hubness problem
Georgiana Dinu, Angeliki Lazaridou, and Marco Baroni. 2015 · 2015
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Bilingual word representations with monolingual quality in mind
Thang Luong, Hieu Pham, and Christopher D. Manning. 2015 · 2015
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Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, and Ian J. Goodfellow. 2015 · 2015
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Normalized word embedding and orthogonal transform for bilingual word translation
Chao Xing, Dong Wang, Chao Liu, and Yiye Lin. 2015 · 2015
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Learning principled bilingual mappings of word embeddings while preserving monolingual invariance
Mikel Artetxe, Gorka Labaka, and Eneko Agirre. 2016 · 2016
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NIPS 2016 tutorial: Generative adversarial networks
Ian J. Goodfellow. 2017 · 2016
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Towards cross-lingual distributed representations without parallel text trained with adversarial autoencoders
Antonio Valerio Miceli Barone. 2016 · 2016
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Learning bilingual word embeddings with (almost) no bilingual data
Mikel Artetxe, Gorka Labaka, and Eneko Agirre. 2017 · 2017
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Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017 · 2017
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A survey of cross-lingual word embedding models
Sebastian Ruder, Ivan Vulic, and Anders Sogaard. 2017 · 2017
Cited alongside, same era.
Gromov-wasserstein alignment of word embedding spaces
David Alvarez-Melis and Tommi Jaakkola. 2018 · 2018
Later among the works it cites.
Word translation without parallel data
Alexis Conneau, Guillaume Lample, Marc’Aurelio Ranzato, Ludovic Denoyer, and Hervé Jégou. 2018 · 2018
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Non-adversarial unsupervised word translation
Yedid Hoshen and Lior Wolf. 2018 · 2018
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Phrase-based & neural unsupervised machine translation
Guillaume Lample, Myle Ott, Alexis Conneau, Ludovic Denoyer, and Marc’Aurelio Ranzato. 2018b · 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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Samuel L. Smith, David H. P. Turban, Steven Hamblin, and Nils Y. Hammerla. 2017 · 2017
Cited alongside, same era.
Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. 2017 · 2017
Cited alongside, same era.
Gromov-wasserstein alignment of word embedding spaces
David Alvarez and Tommi Jaakkola. 2018 · 2018
Cited alongside, same era.
Generalizing and improving bilingual word embedding mappings with a multi-step framework of linear transformations
Mikel Artetxe, Gorka Labaka, and Eneko Agirre. 2018a
Cited in the paper.
A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings
Mikel Artetxe, Gorka Labaka, and Eneko Agirre. 2018b
Cited in the paper.
Unsupervised machine translation using monolingual corpora only
Guillaume Lample, Alexis Conneau, Ludovic Denoyer, and Marc’Aurelio Ranzato. 2018a
Cited in the paper.
Exploiting similarities among languages for machine translation
Tomas Mikolov, Quoc V. Le, and Ilya Sutskever. 2013a
Cited in the paper.
Unsupervised cross-lingual transfer of word embedding spaces
Ruochen Xu, Yiming Yang, Naoki Otani, and Yuexin Wu. 2018a · 2018
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Unsupervised cross-lingual transfer of word embedding spaces
Ruochen Xu, Yiming Yang, Naoki Otani, and Yuexin Wu. 2018b · 2018
Later among the works it cites.