@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://aclanthology.org/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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T UCSMNLP: Statistical Machine Translation for WAT 2019
%A Thida, Aye
%A Han, Nway Nway
%A Oo, Sheinn Thawtar
%A Htar, Khin Thet
%Y Nakazawa, Toshiaki
%Y Ding, Chenchen
%Y Dabre, Raj
%Y Kunchukuttan, Anoop
%Y Doi, Nobushige
%Y Oda, Yusuke
%Y Bojar, Ondřej
%Y Parida, Shantipriya
%Y Goto, Isao
%Y Mino, Hidaya
%S Proceedings of the 6th Workshop on Asian Translation
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F thida-etal-2019-ucsmnlp
%X 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.
%R 10.18653/v1/D19-5210
%U https://aclanthology.org/D19-5210
%U https://doi.org/10.18653/v1/D19-5210
%P 94-98
Markdown (Informal)
[UCSMNLP: Statistical Machine Translation for WAT 2019](https://aclanthology.org/D19-5210) (Thida et al., WAT 2019)
ACL