HyperAIHyperAI
HomeNewsLatest PapersTutorialsDatasetsWikiSOTALLM ModelsGPU LeaderboardEvents
Search
About
HyperAIHyperAI

Machine Translation On Wmt2016 English German

Metrics

BLEU score

Results

Performance results of various models on this benchmark

Model Name
BLEU score
Paper TitleRepository
FLAN 137B (few-shot, k=11)26.1Finetuned Language Models Are Zero-Shot Learners-
Exploiting Mono at Scale (single)-Exploiting Monolingual Data at Scale for Neural Machine Translation-
FLAN 137B (zero-shot)27.0Finetuned Language Models Are Zero-Shot Learners-
SMT + iterative backtranslation (unsupervised) 18.23Unsupervised Statistical Machine Translation-
Unsupervised S2S with attention9.64Unsupervised Machine Translation Using Monolingual Corpora Only-
MADL40.68Multi-Agent Dual Learning-
BiRNN + GCN (Syn + Sem)24.9Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks-
Transformer26.7On the adequacy of untuned warmup for adaptive optimization-
DeLighT28.0DeLighT: Deep and Light-weight Transformer-
Attentional encoder-decoder + BPE34.2Edinburgh Neural Machine Translation Systems for WMT 16-
Unsupervised NMT + weight-sharing10.86Unsupervised Neural Machine Translation with Weight Sharing-
Linguistic Input Features28.4Linguistic Input Features Improve Neural Machine Translation-
0 of 12 row(s) selected.
Machine Translation On Wmt2016 English German | SOTA | HyperAI