Pervasive Attention: 2
Maha Elbayad, Laurent Besacier, Jakob J. Verbeek · 2018
Current state-of-the-art machine translation systems are based on encoder-decoder architectures, that first encode the input sequence, and then generate an output sequence based on the input encoding.Both are interfaced with an attention mechanism that recombines a fixed encoding of the source tokens based on the decoder state.We propose an alternative approach which instead relies on a single 2D convolutional neural network across both sequences.Each layer of our network recodes source tokens on the basis of the output sequence produced so far.Attention-like properties are therefore pervasive throughout the network.Our model yields excellent results, outperforming state-of-the-art encoderdecoder systems, while being conceptually simpler and having fewer parameters.