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Machine Translation On Iwslt2015 German

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BLEU score

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	BLEU score	Paper Title	Repository
Word-level CNN w/attn, input feeding	24.0	Sequence-to-Sequence Learning as Beam-Search Optimization
Conv-LSTM (deep+pos)	30.4	A Convolutional Encoder Model for Neural Machine Translation
QRNN	19.41	Quasi-Recurrent Neural Networks
FlowSeq-base	24.75	FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow
Denoising autoencoders (non-autoregressive)	32.43	Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement
Word-level LSTM w/attn	20.2	Sequence Level Training with Recurrent Neural Networks
ConvS2S	32.31	Convolutional Sequence to Sequence Learning
Transformer with FRAGE	33.97	FRAGE: Frequency-Agnostic Word Representation
ConvS2S+Risk	32.93	Classical Structured Prediction Losses for Sequence to Sequence Learning
RNNsearch	29.98	An Actor-Critic Algorithm for Sequence Prediction
Bi-GRU (MLE+SLE)	28.53	Neural Machine Translation by Jointly Learning to Align and Translate
Pervasive Attention	34.18	Pervasive Attention: 2D Convolutional Neural Networks for Sequence-to-Sequence Prediction
NPMT + language model	30.08	Towards Neural Phrase-based Machine Translation
PS-KD	36.20	Self-Knowledge Distillation with Progressive Refinement of Targets
DCCL	29.56	Compressing Word Embeddings via Deep Compositional Code Learning

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