A Hybrid Approach For Spoken Language Machine Translation
Wenhan Chao1, Zhoujun Li, Yuexin Chen
1School of Computer Science, National University of Defense Technology, Changsha, Hunan, P.R.China
Available Online October 2007.
- 10.2991/iske.2007.197How to use a DOI?
- SMT, EBMT, Re-Ordering model
In this paper, we propose a hybrid approach, which is a statistical machine translation (SMT), while using an example-based decoder. In this way, it will solve efficiently the re-ordering problem in SMT and the problems for spoken language MT, such as lots of omissions, idioms etc. We present a novel re-ordering model for SMT firstly and then an example-based decoder. Through experiments, we show that this approach obtains significant improvements over the baseline on a Chinese-English spoken language translation task.
- © 2007, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Wenhan Chao AU - Zhoujun Li AU - Yuexin Chen PY - 2007/10 DA - 2007/10 TI - A Hybrid Approach For Spoken Language Machine Translation BT - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007) PB - Atlantis Press SP - 1154 EP - 1160 SN - 1951-6851 UR - https://doi.org/10.2991/iske.2007.197 DO - 10.2991/iske.2007.197 ID - Chao2007/10 ER -