Machine Translation On Wmt2016 German English
Metrics
BLEU score
Results
Performance results of various models on this benchmark
Model Name | BLEU score | Paper Title | Repository |
|---|---|---|---|
| Linguistic Input Features | 32.9 | Linguistic Input Features Improve Neural Machine Translation | |
| FLAN 137B (zero-shot) | 38.9 | Finetuned Language Models Are Zero-Shot Learners | |
| Exploiting Mono at Scale (single) | - | Exploiting Monolingual Data at Scale for Neural Machine Translation | - |
| Attentional encoder-decoder + BPE | 38.6 | Edinburgh Neural Machine Translation Systems for WMT 16 | |
| Unsupervised NMT + weight-sharing | 14.62 | Unsupervised Neural Machine Translation with Weight Sharing | |
| Unsupervised S2S with attention | 13.33 | Unsupervised Machine Translation Using Monolingual Corpora Only | |
| SMT + iterative backtranslation (unsupervised) | 23.05 | Unsupervised Statistical Machine Translation | |
| FLAN 137B (few-shot, k=11) | 40.7 | Finetuned Language Models Are Zero-Shot Learners |
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