e-Article
A reordering model for Vietnamese-English statistical machine translation using dependency information
Document Type
Conference
Author
Source
2016 IEEE RIVF International Conference on Computing & Communication Technologies, Research, Innovation, and Vision for the Future (RIVF) Computing & Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), 2016 IEEE RIVF International Conference on. :125-130 Nov, 2016
Subject
Language
Abstract
Reordering is a major challenge in machine translation (MT) between two languages with significant differences in word order. In this paper, we present an approach to learn reordering rules as pre-processing step based on a dependency parser in phrase-based statistical machine translation (SMT) from Vietnamese to English. Dependency parser and transformation rules are used to reorder the source sentence and applied for systems translating Vietnamese to English. We evaluated our approach on Vietnamese-English machine translation tasks, and showed that it outperform the baseline phrase-based SMT system.