Cross-lingual Text Classification
Cross-Lingual Text Classification is a natural language processing task that involves categorizing textual content into predefined classes across different languages by leveraging cross-lingual representations or knowledge transfer, thereby enabling classification in a target language with limited or no labeled training data through its distinction from monolingual text classification.
375
Publications
21.1K
Citations
1.1K
Authors
376
Institutions
Publications per year
2017–2026
215
Parents
375
Cross-lingual Language Model Pretraining
Guillaume Lample, Alexis Conneau · arXiv (Cornell University) · 2019 · 1.6K citations · Full text
XNLI: Evaluating Cross-lingual Sentence Representations
Alexis Conneau, Ruty Rinott, Guillaume Lample et al. · 2018 · 934 citations · Full text
Co-training for cross-lingual sentiment classification
Xiaojun Wan · 2009 · 502 citations · Full text
1–5 of 375
1.1K
12
h-index
Rutgers, The State University of New Jersey, United States
7 publications · 309 citations
7
h-index
5
h-index
5
h-index
5
h-index
1–5 of 1.1K
376

Peking University
Beijing, China
14 h-index
28 publications
1.4K citations
Carnegie Mellon University
Pittsburgh, United States
13 h-index
35 publications
1.9K citations

Chinese Academy of Sciences
Beijing, China
7 h-index
19 publications
526 citations
University of Cambridge
Cambridge, United Kingdom
7 h-index
15 publications
505 citations
Microsoft Research Asia (China)
Beijing, China
7 h-index
20 publications
1.9K citations

Harbin Institute of Technology
Harbin, China
6 h-index
20 publications
666 citations

Beihang University
Beijing, China
6 h-index
18 publications
484 citations
Columbia University
New York, United States
6 h-index
19 publications
652 citations
1–8 of 376
Repository · 31 publications · 3.6K citations
20
h-index
Conference · 9 publications · 321 citations
9
h-index
Conference · 8 publications · 451 citations
9
h-index
5 publications · 223 citations
5
h-index
4 publications · 134 citations
4
h-index
1–5