A word alignment model based on multiobjective evolutionary algorithms

✍ Scribed by Yidong Chen; Xiaodong Shi; Changle Zhou; Qingyang Hong

• Publisher: Elsevier Science
• Year: 2009
• Tongue: English
• Weight: 450 KB
• Volume: 57
• Category: Article
• ISSN: 0898-1221
• DOI: 10.1016/j.camwa.2008.10.018

✦ Synopsis

translation (SMT) Word alignment a b s t r a c t Word alignment is a key task in statistical machine translation (SMT). This paper presents a novel model for this task. In this model, word alignment is considered as a multiobjective optimization problem and solved based on the non-dominated sorting genetic algorithm II (NSGA-II), which is one of the best multiobjective evolutionary algorithms (MOEA). There are two advantages of the proposed model based on NSGA-II. First, it could be easily extended through incorporating new objective functions. Secondly, it does not need any hand-aligned word-level alignment data to determine the weight of each objective function. Experiments were carried out and the results show that the proposed model outperforms the IBM translation models significantly.