Abstract
In phrase-based statistical machine translation, long distance reordering problem is one of the most challenging issues when translating syntactically distant language pairs. In this paper, we propose a novel reordering model to solve this problem. In our model, reordering is affected by the overall structures of sentences such as listings, reduplications, and modifications as well as the relationships of adjacent phrases. To this end, we reflect global syntactic contexts including the parts that are not yet translated during the decoding process.
Original language | English |
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Pages (from-to) | 1694-1698 |
Number of pages | 5 |
Journal | IEICE Transactions on Information and Systems |
Volume | E97-D |
Issue number | 6 |
DOIs | |
Publication status | Published - 2014 Jun |
Keywords
- Global syntactic tree features
- Phrase reordering model
- Phrase-based statistical machine translation
ASJC Scopus subject areas
- Software
- Hardware and Architecture
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering
- Artificial Intelligence