Boosting-Based System Combination for Machine Translation
Tong Xiao, Jingbo Zhu, Muhua Zhu, Huizhen Wang · 2010
In this paper, we present a simple and effective method to address the issue of how to generate diversified translation systems from a single Statistical Machine Translation (SMT) engine for system combination. Our method is based on the framework of boosting. First, a se-quence of weak translation systems is gener-ated from a baseline system in an iterative manner. Then, a strong translation system is built from the ensemble of these weak transla-tion systems. To adapt boosting to SMT sys-tem combination, several key components of the original boosting algorithms are redes-igned in this work. We evaluate our method on Chinese-to-English Machine Translation (MT) tasks in three baseline systems, including a phrase-based system, a hierarchical phrase-based system and a syntax-based system. The experimental results on three NIST evaluation test sets show that our method leads to signifi-cant improvements in translation accuracy over the baseline systems. 1