Abstract
This paper describes the UCSYNLP-Lab submission to WAT 2019 for Myanmar-English translation tasks in both direction. We have used the neural machine translation systems with attention model and utilized the UCSY-corpus and ALT corpus. In NMT with attention model, we use the word segmentation level as well as syllable segmentation level. Especially, we made the UCSY-corpus to be cleaned in WAT 2019. Therefore, the UCSY corpus for WAT 2019 is not identical to those used in WAT 2018. Experiments show that the translation systems can produce the substantial improvements.- Anthology ID:
- D19-5226
- 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:
- 195–199
- Language:
- URL:
- https://aclanthology.org/D19-5226
- DOI:
- 10.18653/v1/D19-5226
- Cite (ACL):
- Yimon ShweSin, Win Pa Pa, and KhinMar Soe. 2019. UCSYNLP-Lab Machine Translation Systems for WAT 2019. In Proceedings of the 6th Workshop on Asian Translation, pages 195–199, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- UCSYNLP-Lab Machine Translation Systems for WAT 2019 (ShweSin et al., WAT 2019)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/D19-5226.pdf