Boosting-Based System Combination for Machine Translation

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 739–748, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Boosting-based System Combination for Machine Translation Tong Xiao, Jingbo Zhu, Muhua Zhu, Huizhen Wang Natural Language Processing Lab. Northeastern University, China {xiaotong,zhujingbo,wanghuizhen}@mail.neu.edu.cn zhumuhua@gmail.com Abstract 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 Introduction Recent research on Statistical Machine Transla- tion (SMT) has achieved substantial progress. Many SMT frameworks have been developed, including phrase-based SMT (Koehn et al., 2003), hierarchical phrase-based SMT (Chiang, 2005), syntax-based SMT (Eisner, 2003; Ding and Palmer, 2005; Liu et al., 2006; Galley et al., 2006; Cowan et al., 2006), etc. With the emergence of various structurally different SMT systems, more and more studies are focused on combining mul- tiple SMT systems for achieving higher transla- tion accuracy rather than using a single transla- tion system. The basic idea of system combination is to ex- tract or generate a translation by voting from an ensemble of translation outputs. Depending on how the translation is combined and what voting strategy is adopted, several methods can be used for system combination, e.g. sentence-level com- bination (Hildebrand and Vogel, 2008) simply selects one from original translations, while some more sophisticated methods, such as word- level and phrase-level combination (Matusov et al., 2006; Rosti et al., 2007), can generate new translations differing from any of the original translations. One of the key factors in SMT system combi- nation is the diversity in the ensemble of transla- tion outputs (Macherey and Och, 2007). To ob- tain diversified translation outputs, most of the current system combination methods require multiple translation engines based on different models. However, this requirement cannot be met in many cases, since we do not always have the access to multiple SMT engines due to the high cost of developing and tuning SMT systems. To reduce the burden of system development, it might be a nice way to combine a set of transla- tion systems built from a single translation en- gine. A key issue here is how to generate an en- semble of diversified translation systems from a single translation engine in …

Content truncated to 500 words