Character-based Neural Machine Translation

Understand

Neural Machine Translation (MT) has reached state-of-the-art results.

• However, one of the main challenges that neural MT still faces is dealing with very large vocabularies and morphologically rich languages.
• In this paper, we propose a neural MT system using character-based embeddings in combination with convolutional and highway layers to replace the standard lookup-based word representations.
• The resulting unlimited-vocabulary and affix-aware source word embeddings are tested in a state-of-the-art neural MT based on an attention-based bidirectional recurrent neural network.