Cross-lingual Representation Learning
Cross-Lingual Representation Learning is a methodological framework in multilingual natural language processing that enables the alignment and integration of semantic representations across different languages into a shared vector space, facilitating knowledge transfer and cross-lingual understanding.
438
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28.1K
Citations
1.2K
Authors
317
Institutions
Publications per year
2017–2026
326
438
How Multilingual is Multilingual BERT?
Telmo Pires, Eva Schlinger, Dan Garrette · 2019 · 1.2K citations · Full text
Language Studies, Linguistics, Computational Linguistics +16
XNLI: Evaluating Cross-lingual Sentence Representations
Alexis Conneau, Ruty Rinott, Guillaume Lample et al. · 2018 · 934 citations · Full text
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Johns Hopkins University
Baltimore, United States
17 h-index
45 publications
3.3K citations

Harbin Institute of Technology
Harbin, China
15 h-index
65 publications
2.9K citations
Google (United States)
Mountain View, United States
15 h-index
80 publications
10.7K citations
University of Cambridge
Cambridge, United Kingdom
14 h-index
36 publications
2.9K citations
University of Copenhagen
Copenhagen, Denmark
13 h-index
41 publications
3K citations
Carnegie Mellon University
Pittsburgh, United States
12 h-index
32 publications
3.7K citations
Microsoft (United States)
Redmond, United States
11 h-index
56 publications
5.3K citations
Microsoft Research Asia (China)
Beijing, China
11 h-index
36 publications
2.6K citations
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Repository · 67 publications · 3.5K citations
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Conference · 14 publications · 524 citations
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