Character based Neural Machine Translation
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. The proposed MT scheme provides improved results even when the source language is not morphologically rich. Improvements up to 3 BLEU points are obtained in the German English WMT task.
ACL