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Linguistic Annotation

Moving neural machine translation towards models that are based on linguistic insight into language include adding linguistic annotation at the word level or model syntactic or semantic structure.

Linguistic Annotation is the main subject of 11 publications.

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Wu et al. (2012) propose to use factored representations of words (using lemma, stem, and part of speech), with each factor encoded in a one-hot vector, in the input to a recurrent neural network language model. Sennrich and Haddow (2016) use such representations in the input and output of neural machine translation models, demonstrating better translation quality.

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Linguistic Features

  • Eriguchi et al. (2016)
  • Martínez et al. (2016)
  • Zhang et al. (2016)
  • Yamagishi et al. (2016)

Syntactic Models

  • Chen et al. (2017)
  • Li et al. (2017)
  • Wu et al. (2017)
  • Aharoni and Goldberg (2017)
  • Eriguchi et al. (2017)

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