Pervasive Attention: 2D Convolutional Neural Networks for Sequence-to-Sequence Prediction

Code

• github.com/elbayadm/attn2d
  OfficialIn paperpytorch★ 0
• github.com/poifull10/PervasiveAttentionNet
  none★ 0
• github.com/tdiggelm/nn-experiments
  none★ 0

Abstract

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 re-codes 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 encoder-decoder systems, while being conceptually simpler and having fewer parameters.

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