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CROSS-LINGUAL TRANSFER WITH CLASS-WEIGHTED LANGUAGE-INVARIANT REPRESENTATIONS
Ruicheng Xian
,
Heng Ji
,
Han Zhao
Siebel School of Computing and Data Science
Coordinated Science Lab
National Center for Supercomputing Applications (NCSA)
Carl R. Woese Institute for Genomic Biology
Electrical and Computer Engineering
Research output
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Contribution to conference
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peer-review
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Dive into the research topics of 'CROSS-LINGUAL TRANSFER WITH CLASS-WEIGHTED LANGUAGE-INVARIANT REPRESENTATIONS'. Together they form a unique fingerprint.
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Computer Science
Invariant Representation
100%
Target Language
100%
Performance Gain
50%
Learning Technique
50%
Semisupervised Learning
50%
Affect Performance
50%
Language Modeling
50%
Source Language
50%
Zero-Shot Learning
50%
Social Sciences
Learning Method
100%
Multilingualism
100%
Language Modeling
100%
Zero-Shot Learning
100%
Semisupervised Learning
100%
Keyphrases
Downstream Transfer
50%