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Creators/Authors contains: "Medina, Julian"

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  1. ; (, International Conference on Machine Learning Applications)
    Recent papers in neural machine translation have proposed the strict use of attention mechanisms over previous stan- dards such as recurrent and convolutional neural networks (RNNs and CNNs). We propose that by running traditionally stacked encoding branches from encoder-decoder attention- focused architectures in parallel, that even more sequential operations can be removed from the model and thereby de- crease training time. In particular, we modify the recently published attention-based architecture called Transformer by Google, by replacing sequential attention modules with par- allel ones, reducing the amount of training time and substan- tially improving BLEU scores at the same time. Experiments over the English to German and English to French translation tasks show that our model establishes a new state of the art. 
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