@inproceedings{thida-etal-2019-ucsmnlp,
    title = "{UCSMNLP}: Statistical Machine Translation for {WAT} 2019",
    author = "Thida, Aye  and
      Han, Nway Nway  and
      Oo, Sheinn Thawtar  and
      Htar, Khin Thet",
    editor = "Nakazawa, Toshiaki  and
      Ding, Chenchen  and
      Dabre, Raj  and
      Kunchukuttan, Anoop  and
      Doi, Nobushige  and
      Oda, Yusuke  and
      Bojar, Ond{\v{r}}ej  and
      Parida, Shantipriya  and
      Goto, Isao  and
      Mino, Hidaya",
    booktitle = "Proceedings of the 6th Workshop on Asian Translation",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/D19-5210/",
    doi = "10.18653/v1/D19-5210",
    pages = "94--98",
    abstract = "This paper represents UCSMNLP`s submission to the WAT 2019 Translation Tasks focusing on the Myanmar-English translation. Phrase based statistical machine translation (PBSMT) system is built by using other resources: Name Entity Recognition (NER) corpus and bilingual dictionary which is created by Google Translate (GT). This system is also adopted with listwise reranking process in order to improve the quality of translation and tuning is done by changing initial distortion weight. The experimental results show that PBSMT using other resources with initial distortion weight (0.4) and listwise reranking function outperforms the baseline system."
}