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Models/XLMft UDA

XLMft UDA

Reported on 8 benchmarks across 2 tasks · 1 paper · 8 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Natural Language Processing8 results

Cross-LingualonMLDoc Zero-Shot English-to-French
Accuracy· 2019-09-16
96.05
SOTA
Bridging the domain gap in cross-lingual document classification arXiv:1909.07009
Cross-LingualonMLDoc Zero-Shot English-to-Chinese
Accuracy· 2019-09-16
93.32
SOTA
Bridging the domain gap in cross-lingual document classification arXiv:1909.07009
Cross-LingualonMLDoc Zero-Shot English-to-Spanish
Accuracy· 2019-09-16
96.8
SOTA
Bridging the domain gap in cross-lingual document classification arXiv:1909.07009
Cross-LingualonMLDoc Zero-Shot English-to-Russian
Accuracy· 2019-09-16
89.7
SOTA
Bridging the domain gap in cross-lingual document classification arXiv:1909.07009
Cross-Lingual Document ClassificationonMLDoc Zero-Shot English-to-French
Accuracy· 2019-09-16
96.05
SOTA
Bridging the domain gap in cross-lingual document classification arXiv:1909.07009
Cross-Lingual Document ClassificationonMLDoc Zero-Shot English-to-Chinese
Accuracy· 2019-09-16
93.32
SOTA
Bridging the domain gap in cross-lingual document classification arXiv:1909.07009
Cross-Lingual Document ClassificationonMLDoc Zero-Shot English-to-Spanish
Accuracy· 2019-09-16
96.8
SOTA
Bridging the domain gap in cross-lingual document classification arXiv:1909.07009
Cross-Lingual Document ClassificationonMLDoc Zero-Shot English-to-Russian
Accuracy· 2019-09-16
89.7
SOTA
Bridging the domain gap in cross-lingual document classification arXiv:1909.07009