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In this paper, we focus on the problem of adapting word vector-based models to new textual data.
A generalized solution of the orthogonal procrustes problem
Peter H. Schönemann. 1966 · 1966
Earlier work this paper cites.
Improving zero-shot learning by mitigating the hubness problem
Georgiana Dinu, Angeliki Lazaridou, and Marco Baroni. 2014 · 2014
Earlier work this paper cites.
Normalized word embedding and orthogonal trans- form for bilingual word translation
Chao Xing, Dong Wang, Chao Liu, and Yiye Lin. 2015 · 2015
Earlier work this paper cites.
Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
Cited alongside, same era.
Bag of tricks for efficient text classification
Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov. 2016 · 2016
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.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013a
Cited in the paper.
Exploiting similarities among languages for machine translation
Tomas Mikolov, Quoc V Le, and Ilya Sutskever. 2013b
Cited in the paper.
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.
Learning word vectors for 157 languages
Edouard Grave, Piotr Bojanowski, Prakhar Gupta, Armand Joulin, and Tomas Mikolov. 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.
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