RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition
Table 3: Comparison on German→English.
['toolkit', 'BLEU [%] 2015', 'BLEU [%] 2017']
We report the best performing Sockeye model we trained, which has 1 bidirectional and 3 unidirectional encoder layers, 1 pre-attention target recurrent layer, and 1 post-attention decoder layer. We trained with a max sequence length of 75, and used the ‘coverage’ RNN attention type. For Sockeye, the final model is an average of the 4 best runs according to the development perplexity. We obtain the best results with Sockeye using a Transformer network model Vaswani et al. , where we achieve 32.0% BLEU on newstest2017.