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Chen, Yidong; Shi, Xiaodong; Zhou, Changle; Hong, Qingyang

A word alignment model based on multiobjective evolutionary algorithms. (English) Zbl 1186.90099

Comput. Math. Appl. 57, No. 11-12, 1724-1729 (2009).
Summary: 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.

MSC:

90C29 Multi-objective and goal programming
90C59 Approximation methods and heuristics in mathematical programming
68T05 Learning and adaptive systems in artificial intelligence

Keywords:

multiobjective optimization; multiobjective evolutionary algorithms (MOEA); statistical machine translation (SMT); word alignment

Software:

NSGA-II
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Full Text: DOI

References:

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