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Machine Translation On Wmt2016 English German

Metrics

BLEU score

Results

Performance results of various models on this benchmark

		Paper Title	Repository
MADL	40.68	Multi-Agent Dual Learning	-
Attentional encoder-decoder + BPE	34.2	Edinburgh Neural Machine Translation Systems for WMT 16
Linguistic Input Features	28.4	Linguistic Input Features Improve Neural Machine Translation
DeLighT	28.0	DeLighT: Deep and Light-weight Transformer
FLAN 137B (zero-shot)	27.0	Finetuned Language Models Are Zero-Shot Learners
Transformer	26.7	On the adequacy of untuned warmup for adaptive optimization
FLAN 137B (few-shot, k=11)	26.1	Finetuned Language Models Are Zero-Shot Learners
BiRNN + GCN (Syn + Sem)	24.9	Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks	-
SMT + iterative backtranslation (unsupervised)	18.23	Unsupervised Statistical Machine Translation
Unsupervised NMT + weight-sharing	10.86	Unsupervised Neural Machine Translation with Weight Sharing
Unsupervised S2S with attention	9.64	Unsupervised Machine Translation Using Monolingual Corpora Only
Exploiting Mono at Scale (single)	-	Exploiting Monolingual Data at Scale for Neural Machine Translation	-

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Machine Translation On Wmt2016 English German | SOTA | HyperAI