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.- Anthology ID:
- D19-5210
- Volume:
- Proceedings of the 6th Workshop on Asian Translation
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Toshiaki Nakazawa, Chenchen Ding, Raj Dabre, Anoop Kunchukuttan, Nobushige Doi, Yusuke Oda, Ondřej Bojar, Shantipriya Parida, Isao Goto, Hidaya Mino
- Venue:
- WAT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 94–98
- Language:
- URL:
- https://aclanthology.org/D19-5210
- DOI:
- 10.18653/v1/D19-5210
- Cite (ACL):
- Aye Thida, Nway Nway Han, Sheinn Thawtar Oo, and Khin Thet Htar. 2019. UCSMNLP: Statistical Machine Translation for WAT 2019. In Proceedings of the 6th Workshop on Asian Translation, pages 94–98, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- UCSMNLP: Statistical Machine Translation for WAT 2019 (Thida et al., WAT 2019)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/D19-5210.pdf