Weighted Transformer Network for Machine Translation

Most neural architectures for machine translation use an encoder-decoder model consisting of either convolutional or recurrent layers. The encoder layers map the input to a latent space and the decoder, in turn, uses this latent representation to map the inputs to the targets. Such sequence-to-sequence tasks have been quite successful