Linguistic Input Features Improve Neural Machine Translation

Understand

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 the baseline word feature.
• We add morphological features, part-of-speech tags, and syntactic dependency labels as input features to English<->German, and English->Romanian neural machine translation systems.