Sequence-to-sequence neural network models for transliteration
Computation and Language
2016-11-01 v1
Abstract
Transliteration is a key component of machine translation systems and software internationalization. This paper demonstrates that neural sequence-to-sequence models obtain state of the art or close to state of the art results on existing datasets. In an effort to make machine transliteration accessible, we open source a new Arabic to English transliteration dataset and our trained models.
Cite
@article{arxiv.1610.09565,
title = {Sequence-to-sequence neural network models for transliteration},
author = {Mihaela Rosca and Thomas Breuel},
journal= {arXiv preprint arXiv:1610.09565},
year = {2016}
}