Machine Translation On Iwslt2014 German

評価指標

BLEU score

評価結果

このベンチマークにおける各モデルのパフォーマンス結果

モデル名	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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