@inproceedings{le-sadat-2018-low,
    title = "Low-Resource Machine Transliteration Using Recurrent Neural Networks of {A}sian Languages",
    author = "Le, Ngoc Tan  and
      Sadat, Fatiha",
    editor = "Chen, Nancy  and
      Banchs, Rafael E.  and
      Duan, Xiangyu  and
      Zhang, Min  and
      Li, Haizhou",
    booktitle = "Proceedings of the Seventh Named Entities Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/W18-2414/",
    doi = "10.18653/v1/W18-2414",
    pages = "95--100",
    abstract = "Grapheme-to-phoneme models are key components in automatic speech recognition and text-to-speech systems. With low-resource language pairs that do not have available and well-developed pronunciation lexicons, grapheme-to-phoneme models are particularly useful. These models are based on initial alignments between grapheme source and phoneme target sequences. Inspired by sequence-to-sequence recurrent neural network-based translation methods, the current research presents an approach that applies an alignment representation for input sequences and pre-trained source and target embeddings to overcome the transliteration problem for a low-resource languages pair. We participated in the NEWS 2018 shared task for the English-Vietnamese transliteration task."
}