Cross-lingual Transfer Learning
Cross-Lingual Transfer Learning is a machine learning paradigm in which knowledge acquired from a source language model is leveraged to improve performance in a target language model, enabling effective natural language processing across linguistic boundaries.
692
Publications
37.6K
Citations
1.9K
Authors
460
Institutions
Publications per year
2017–2026
533
692
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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460
Carnegie Mellon University
Pittsburgh, United States
22 h-index
105 publications
7.8K citations
University of Cambridge
Cambridge, United Kingdom
20 h-index
67 publications
4.6K citations
Google (United States)
Mountain View, United States
19 h-index
80 publications
13.5K citations
Microsoft Research Asia (China)
Beijing, China
19 h-index
75 publications
3.9K citations

Johns Hopkins University
Baltimore, United States
15 h-index
55 publications
3.7K citations
University of Mannheim
Mannheim, Germany
14 h-index
27 publications
917 citations
Microsoft (United States)
Redmond, United States
14 h-index
72 publications
8.8K citations
University of Copenhagen
Copenhagen, Denmark
13 h-index
26 publications
2.3K citations
1–8 of 460
Repository · 94 publications · 4.7K citations
32
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Conference · 24 publications · 883 citations
17
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Conference · 17 publications · 1K citations
15
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Conference · 14 publications · 616 citations
12
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TASLP · 9 publications · 248 citations
9
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