@inproceedings{grundkiewicz-heafield-2018-neural,
    title = "Neural Machine Translation Techniques for Named Entity Transliteration",
    author = "Grundkiewicz, Roman  and
      Heafield, Kenneth",
    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/W18-2413/",
    doi = "10.18653/v1/W18-2413",
    pages = "89--94",
    abstract = "Transliterating named entities from one language into another can be approached as neural machine translation (NMT) problem, for which we use deep attentional RNN encoder-decoder models. To build a strong transliteration system, we apply well-established techniques from NMT, such as dropout regularization, model ensembling, rescoring with right-to-left models, and back-translation. Our submission to the NEWS 2018 Shared Task on Named Entity Transliteration ranked first in several tracks."
}