Linguistic Input Features Improve Neural Machine Translation

@inproceedings{Sennrich2016LinguisticIF,
  title={Linguistic Input Features Improve Neural Machine Translation},
  author={Rico Sennrich and Barry Haddow},
  booktitle={WMT},
  year={2016}
}
Neural machine translation has recently achieved impressive results, while using little in the way of external linguistic information. In this paper we show that the strong learning capability of neural MT models does not make linguistic features redundant; they can be easily incorporated to provide further improvements in performance. We generalize the embedding layer of the encoder in the attentional encoder–decoder architecture to support the inclusion of arbitrary features, in addition to… CONTINUE READING
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