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Recent approaches to cross-lingual word embedding have generally been based on linear transformations between the sets of embedding vectors in the two languages.
Earth mover’s distance minimization for unsupervised bilingual lexicon induction
Meng Zhang, Yang Liu, Huanbo Luan, and Maosong Sun. 2017 · 1945
Earlier work this paper cites.
Learning bilingual lexicons from monolingual corpora
Aria Haghighi, Percy Liang, Taylor Berg-Kirkpatrick, and Dan Klein. 2008 · 2008
Earlier work this paper cites.
Hubs in space: Popular nearest neighbors in high-dimensional data
Miloš Radovanović, Alexandros Nanopoulos, and Mirjana Ivanović. 2010 · 2010
Earlier work this paper cites.
Inducing crosslingual distributed representations of words
Alexandre Klementiev, Ivan Titov, and Binod Bhattarai. 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.
Improving vector space word representations using multilingual correlation
Manaal Faruqui and Chris Dyer. 2014 · 2014
Earlier work this paper cites.
Multilingual models for compositional distributed semantics
Karl Moritz Hermann and Phil Blunsom. 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Bilbowa: Fast bilingual distributed representations without word alignments
Stephan Gouws, Yoshua Bengio, and Greg Corrado. 2015 · 2015
Earlier work this paper cites.
Cross-lingual dependency parsing based on distributed representations
Jiang Guo, Wanxiang Che, David Yarowsky, Haifeng Wang, and Ting Liu. 2015 · 2015
Earlier work this paper cites.
Variational inference with normalizing flows
Danilo Jimenez Rezende and Shakir Mohamed. 2015 · 2015
Earlier work this paper cites.
Ridge regression, hubness, and zero-shot learning
Yutaro Shigeto, Ikumi Suzuki, Kazuo Hara, Masashi Shimbo, and Yuji Matsumoto. 2015 · 2015
Earlier work this paper cites.
Word representations via gaussian embedding
Luke Vilnis and Andrew McCallum. 2015 · 2015
Earlier work this paper cites.
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.
Learning principled bilingual mappings of word embeddings while preserving monolingual invariance
Mikel Artetxe, Gorka Labaka, and Eneko Agirre. 2016 · 2016
Cited alongside, same era.
Nasari: Integrating explicit knowledge and corpus statistics for a multilingual representation of concepts and entities
José Camacho-Collados, Mohammad Taher Pilehvar, and Roberto Navigli. 2016 · 2016
Cited alongside, same era.
Density estimation using real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. 2016 · 2016
Cited alongside, same era.
Improved variational inference with inverse autoregressive flow
Diederik P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling. 2016 · 2016
Cited alongside, same era.
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 alignment of embeddings with Wasserstein Procrustes
Edouard Grave, Armand Joulin, and Quentin Berthet. 2018 · 2018
Later among the works it cites.
Universal neural machine translation for extremely low resource languages
Jiatao Gu, Hany Hassan, Jacob Devlin, and Victor OK Li. 2018 · 2018
Later among the works it cites.
Unsupervised learning of syntactic structure with invertible neural projections
Junxian He, Graham Neubig, and Taylor Berg-Kirkpatrick. 2018 · 2018
Later among the works it cites.
Non-adversarial unsupervised word translation
Yedid Hoshen and Lior Wolf. 2018 · 2018
Later among the works it cites.
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Cross-lingual models of word embeddings: An empirical comparison
Shyam Upadhyay, Manaal Faruqui, Chris Dyer, and Dan Roth. 2016 · 2016
Cited alongside, same era.
Transfer learning for low-resource neural machine translation
Barret Zoph, Deniz Yuret, Jonathan May, and Kevin Knight. 2016 · 2016
Cited alongside, same era.
Learning bilingual word embeddings with (almost) no bilingual data
Mikel Artetxe, Gorka Labaka, and Eneko Agirre. 2017 · 2017
Cited alongside, same era.
Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017 · 2017
Cited alongside, same era.
Word translation without parallel data
Alexis Conneau, Guillaume Lample, Marc’Aurelio Ranzato, Ludovic Denoyer, and Hervé Jégou. 2017 · 2017
Cited alongside, same era.
Masked autoregressive flow for density estimation
George Papamakarios, Iain Murray, and Theo Pavlakou. 2017 · 2017
Cited alongside, same era.
A survey of cross-lingual embedding models
Sebastian Ruder, Ivan Vulic, and Anders Søgaard. 2017 · 2017
Cited alongside, same era.
Learning multilingual word embeddings in latent metric space: a geometric approach
Pratik Jawanpuria, Arjun Balgovind, Anoop Kunchukuttan, and Bamdev Mishra. 2018 · 2018
Later among the works it cites.
Loss in translation: Learning bilingual word mapping with a retrieval criterion
Armand Joulin, Piotr Bojanowski, Tomas Mikolov, Hervé Jégou, and Edouard Grave. 2018 · 2018
Later among the works it cites.
Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal. 2018 · 2018
Later among the works it cites.
On the limitations of unsupervised bilingual dictionary induction
Anders Søgaard, Sebastian Ruder, and Ivan Vulić. 2018 · 2018
Later among the works it cites.
Unsupervised cross-lingual transfer of word embedding spaces
Ruochen Xu, Yiming Yang, Naoki Otani, and Yuexin Wu. 2018 · 2018
Later among the works it cites.
Near or far, wide range zero-shot cross-lingual dependency parsing
Zhisong Zhang, Wasi Uddin Ahmad, Xuezhe Ma, Eduard Hovy, Kai-Wei Chang, and Nanyun Peng. 2018 · 2018
Later among the works it cites.
Factors influencing the surprising instability of word embeddings
Laura Wendlandt, Jonathan K. Kummerfeld, and Rada Mihalcea. 2018 · 2092
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