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Recent work has managed to learn cross-lingual word embeddings without parallel data by mapping monolingual embeddings to a shared space through adversarial training.
Earth mover’s distance minimization for unsupervised bilingual lexicon induction
Meng Zhang, Yang Liu, Huanbo Luan, and Maosong Sun. 2017b · 1945
Earlier work this paper cites.
Adversarial training for unsupervised bilingual lexicon induction
Meng Zhang, Yang Liu, Huanbo Luan, and Maosong Sun. 2017a · 1970
Earlier work this paper cites.
Cross-lingual induction of selectional preferences with bilingual vector spaces
Yves Peirsman and Sebastian Padó. 2010 · 2010
Earlier work this paper cites.
Parallel data, tools and interfaces in opus
Jörg Tiedemann. 2012 · 2012
Earlier work this paper cites.
Exploiting similarities among languages for machine translation
Tomas Mikolov, Quoc V Le, and Ilya Sutskever. 2013 · 2013
Earlier work this paper cites.
A study on bootstrapping bilingual vector spaces from non-parallel data (and nothing else)
Ivan Vulić and Marie-Francine Moens. 2013 · 2013
Earlier work this paper cites.
Improving vector space word representations using multilingual correlation
Manaal Faruqui and Chris Dyer. 2014 · 2014
Earlier work this paper cites.
Improving zero-shot learning by mitigating the hubness problem
Georgiana Dinu, Angeliki Lazaridou, and Marco Baroni. 2015 · 2015
Cited alongside, same era.
Hubness and pollution: Delving into cross-space mapping for zero-shot learning
Angeliki Lazaridou, Georgiana Dinu, and Marco Baroni. 2015 · 2015
Cited alongside, same era.
Deep multilingual correlation for improved word embeddings
Ang Lu, Weiran Wang, Mohit Bansal, Kevin Gimpel, and Karen Livescu. 2015 · 2015
Cited alongside, same era.
Ridge regression, hubness, and zero-shot learning
Yutaro Shigeto, Ikumi Suzuki, Kazuo Hara, Masashi Shimbo, and Yuji Matsumoto. 2015 · 2015
Cited alongside, same era.
Normalized word embedding and orthogonal transform for bilingual word translation
Chao Xing, Dong Wang, Chao Liu, and Yiye Lin. 2015 · 2015
Cited alongside, same era.
On the role of seed lexicons in learning bilingual word embeddings
Ivan Vulić and Anna Korhonen. 2016 · 2016
Later among the works it cites.
Ten pairs to tag – multilingual pos tagging via coarse mapping between embeddings
Yuan Zhang, David Gaddy, Regina Barzilay, and Tommi Jaakkola. 2016 · 2016
Later among the works it cites.
Learning bilingual word embeddings with (almost) no bilingual data
Mikel Artetxe, Gorka Labaka, and Eneko Agirre. 2017 · 2017
Later among the works it cites.
Offline bilingual word vectors, orthogonal transformations and the inverted softmax
Samuel L Smith, David HP Turban, Steven Hamblin, and Nils Y Hammerla. 2017 · 2017
Later among the works it cites.
Unsupervised neural machine translation
Mikel Artetxe, Gorka Labaka, Eneko Agirre, and Kyunghyun Cho. 2018b · 2018
Closest in time.
Word translation without parallel data
Alexis Conneau, Guillaume Lample, Marc’Aurelio Ranzato, Ludovic Denoyer, and Hervé Jégou. 2018 · 2018
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Learning principled bilingual mappings of word embeddings while preserving monolingual invariance
Mikel Artetxe, Gorka Labaka, and Eneko Agirre. 2016 · 2016
Cited alongside, same era.
Towards cross-lingual distributed representations without parallel text trained with adversarial autoencoders
Antonio Valerio Miceli Barone. 2016 · 2016
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.
Hubs in space: Popular nearest neighbors in high-dimensional data
Miloš Radovanović, Alexandros Nanopoulos, and Mirjana Ivanović. 2010a
Cited in the paper.
On the existence of obstinate results in vector space models
Milos Radovanović, Alexandros Nanopoulos, and Mirjana Ivanović. 2010b
Cited in the paper.
Closest in time.
Unsupervised machine translation using monolingual corpora only
Guillaume Lample, Alexis Conneau, Ludovic Denoyer, and Marc’Aurelio Ranzato. 2018 · 2018
Closest in time.