@inproceedings{singhania-etal-2018-statistical,
    title = "Statistical Machine Transliteration Baselines for {NEWS} 2018",
    author = "Singhania, Snigdha  and
      Nguyen, Minh  and
      Ngo, Gia H.  and
      Chen, Nancy",
    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/Ingest-2025-COMPUTEL/W18-2410/",
    doi = "10.18653/v1/W18-2410",
    pages = "74--78",
    abstract = "This paper reports the results of our trans-literation experiments conducted on NEWS 2018 Shared Task dataset. We focus on creating the baseline systems trained using two open-source, statistical transliteration tools, namely Sequitur and Moses. We discuss the pre-processing steps performed on this dataset for both the systems. We also provide a re-ranking system which uses top hypotheses from Sequitur and Moses to create a consolidated list of transliterations. The results obtained from each of these models can be used to present a good starting point for the participating teams."
}