@InProceedings{le-sadat:2018:NEWS2018,
  author    = {Le, Ngoc Tan  and  Sadat, Fatiha},
  title     = {Low-Resource Machine Transliteration Using Recurrent Neural Networks of Asian Languages},
  booktitle = {Proceedings of the Seventh Named Entities Workshop},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  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.},
  url       = {http://www.aclweb.org/anthology/W18-2414}
}