# Convolutional Sequence to Sequence Learning

Jonas Gehring • Michael Auli • David Grangier • Denis Yarats • Yann N. Dauphin

The prevalent approach to sequence to sequence learning maps an input sequence to a variable length output sequence via recurrent neural networks. We introduce an architecture based entirely on convolutional neural networks. Compared to recurrent models, computations over all elements can be fully parallelized during training and optimization is easier since the number of non-linearities is fixed and independent of the input length.

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