Optical Character Recognition2020en
mldoc-zero-shot-english-to-german
Dataset from Papers With Code
Metrics:accuracy, cer, wer, f1
Current State of the Art
XLMft UDA
Unknown
96.95
accuracy
accuracy Progress Over Time
Showing 3 breakthroughs from May 2018 to Sep 2019
Key Milestones
May 2018
MultiCCA + CNN
From paper: A Corpus for Multilingual Document Classification in Eight Languages
81.2
Dec 2018
Massively Multilingual Sentence Embeddings
From paper: Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond
84.8
+4.4%
Sep 2019
XLMft UDACurrent SOTA
From paper: Bridging the domain gap in cross-lingual document classification
97.0
+14.4%
Total Improvement
19.4%
Time Span
1y 4m
Breakthroughs
3
Current SOTA
97.0
Top Models Performance Comparison
Top 5 models ranked by accuracy
Best Score
97.0
Top Model
XLMft UDA
Models Compared
5
Score Range
25.1
accuracyPrimary
| # | Model | Score | Paper / Code | Date |
|---|---|---|---|---|
| 1 | XLMft UDA | 96.95 | Sep 2019 | |
| 2 | MultiFiT, pseudo | 91.62 | Sep 2019 | |
| 3 | Massively Multilingual Sentence Embeddings | 84.78 | Dec 2018 | |
| 4 | MultiCCA + CNN | 81.2 | May 2018 | |
| 5 | BiLSTM (Europarl) | 71.83 | May 2018 |
Related Papers4
Bridging the domain gap in cross-lingual document classification
Sep 2019Models: XLMft UDA
MultiFiT: Efficient Multi-lingual Language Model Fine-tuning
Sep 2019Models: MultiFiT, pseudo
Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond
Dec 2018Models: Massively Multilingual Sentence Embeddings
A Corpus for Multilingual Document Classification in Eight Languages
May 2018Models: MultiCCA + CNN, BiLSTM (Europarl)