Machine Translation On Iwslt2014 German

Metrics

BLEU score

Results

Performance results of various models on this benchmark

Model Name	BLEU score	Paper Title	Repository
Transformer	34.44	Attention Is All You Need	-
Rfa-Gate-arccos	34.4	Random Feature Attention	-
TaLK Convolutions	35.5	Time-aware Large Kernel Convolutions	-
Bi-SimCut	38.37	Bi-SimCut: A Simple Strategy for Boosting Neural Machine Translation	-
Mask Attention Network (small)	36.3	Mask Attention Networks: Rethinking and Strengthen Transformer	-
Cutoff+Knee	37.78	Wide-minima Density Hypothesis and the Explore-Exploit Learning Rate Schedule	-
CNAT	31.15	Non-Autoregressive Translation by Learning Target Categorical Codes	-
Minimum Risk Training [Edunov2017]	32.84	Classical Structured Prediction Losses for Sequence to Sequence Learning	-
BiBERT	38.61	BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translation	-
LightConv	34.8	Pay Less Attention with Lightweight and Dynamic Convolutions	-
TransformerBase + AutoDropout	35.8	AutoDropout: Learning Dropout Patterns to Regularize Deep Networks	-
DynamicConv	35.2	Pay Less Attention with Lightweight and Dynamic Convolutions	-
Transformer + R-Drop	37.25	R-Drop: Regularized Dropout for Neural Networks	-
Transformer	35.1385	Guidelines for the Regularization of Gammas in Batch Normalization for Deep Residual Networks	-
Back-Translation Finetuning	28.83	Tag-less Back-Translation	-
Local Joint Self-attention	35.7	Joint Source-Target Self Attention with Locality Constraints	-
Transformer + R-Drop + Cutoff	37.90	R-Drop: Regularized Dropout for Neural Networks	-
Data Diversification	37.2	Data Diversification: A Simple Strategy For Neural Machine Translation	-
UniDrop	36.88	UniDrop: A Simple yet Effective Technique to Improve Transformer without Extra Cost	-
Actor-Critic [Bahdanau2017]	28.53	An Actor-Critic Algorithm for Sequence Prediction	-

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