Optical Character Recognition2020en
mldoc-zero-shot-english-to-spanish
Dataset from Papers With Code
Metrics:accuracy, cer, wer, f1
Current State of the Art
XLMft UDA
Unknown
96.8
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
72.5
Dec 2018
Massively Multilingual Sentence Embeddings
From paper: Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond
77.3
+6.7%
Sep 2019
XLMft UDACurrent SOTA
From paper: Bridging the domain gap in cross-lingual document classification
96.8
+25.2%
Total Improvement
33.5%
Time Span
1y 4m
Breakthroughs
3
Current SOTA
96.8
Top Models Performance Comparison
Top 6 models ranked by accuracy
Best Score
96.8
Top Model
XLMft UDA
Models Compared
6
Score Range
30.1
accuracyPrimary
| # | Model | Score | Paper / Code | Date |
|---|---|---|---|---|
| 1 | XLMft UDA | 96.8 | Sep 2019 | |
| 2 | MultiFiT, pseudo | 79.1 | Sep 2019 | |
| 3 | Massively Multilingual Sentence Embeddings | 77.33 | Dec 2018 | |
| 4 | MultiCCA + CNN | 72.5 | May 2018 | |
| 5 | BiLSTM (UN) | 69.5 | May 2018 | |
| 6 | BiLSTM (Europarl) | 66.65 | 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 (UN), BiLSTM (Europarl)