On the Properties of Neural Machine Translation: Encoder–Decoder Approaches

Neural machine translation is a relatively new approach to statistical machine translation based purely on neural networks.The neural machine translation models often consist of an encoder and a decoder.The encoder extracts a fixed-length representation from a variable-length input sentence, and the decoder generates a correct translation from this representation.In this paper, we focus on analyzing the properties of the neural machine translation using two models; RNN Encoder-Decoder and a newly proposed gated recursive convolutional neural network.We show that the neural machine translation performs relatively well on short sentences without unknown words, but its performance degrades rapidly as the length…

• CI
  Canadian Institute for Advanced Research
• CC
  Compute Canada
• CR
  Canada Research Chairs
• NS
  Natural Sciences and Engineering Research Council of Canada