Convolutional Sequence to Sequence Learning

Understand

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.
• Our use of gated linear units eases gradient propagation and we equip each decoder layer with a separate attention module.