Character-based Neural Machine Translation

Marta R. Costa-Jussà; José A. R. Fonollosa

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arXiv · 1603.00810

Character-based Neural Machine Translation

Marta R. Costa-Jussà·José A. R. Fonollosa

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Abstract

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.

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BibTeX RIS

Marta R. Costa-Jussà, José A. R. Fonollosa. 2016-03-02. Character-based Neural Machine Translation. https://arxiv.org/abs/1603.00810

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