Improved Word-Level System Combination for Machine Translation

Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 312–319, Prague, Czech Republic, June 2007. c⃝2007 Association for Computational Linguistics Improved Word-Level System Combination for Machine Translation Antti-Veikko I. Rosti and Spyros Matsoukas and Richard Schwartz BBN Technologies, 10 Moulton Street Cambridge, MA 02138 � arosti,smatsouk,schwartz � @bbn.com Abstract Recently, confusion network decoding has been applied in machine translation system combination. Due to errors in the hypoth- esis alignment, decoding may result in un- grammatical combination outputs. This pa- per describes an improved confusion net- work based method to combine outputs from multiple MT systems. In this approach, ar- bitrary features may be added log-linearly into the objective function, thus allowing language model expansion and re-scoring. Also, a novel method to automatically se- lect the hypothesis which other hypotheses are aligned against is proposed. A generic weight tuning algorithm may be used to op- timize various automatic evaluation metrics including TER, BLEU and METEOR. The experiments using the 2005 Arabic to En- glish and Chinese to English NIST MT eval- uation tasks show significant improvements in BLEU scores compared to earlier confu- sion network decoding based methods. 1 Introduction System combination has been shown to improve classification performance in various tasks. There are several approaches for combining classifiers. In ensemble learning, a collection of simple classifiers is used to yield better performance than any single classifier; for example boosting (Schapire, 1990). Another approach is to combine outputs from a few highly specialized classifiers. The classifiers may be based on the same basic modeling techniques but differ by, for example, alternative feature repre- sentations. Combination of speech recognition out- puts is an example of this approach (Fiscus, 1997). In speech recognition, confusion network decoding (Mangu et al., 2000) has become widely used in sys- tem combination. Unlike speech recognition, current statistical ma- chine translation (MT) systems are based on various different paradigms; for example phrasal, hierarchi- cal and syntax-based systems. The idea of combin- ing outputs from different MT systems to produce consensus translations in the hope of generating bet- ter translations has been around for a while (Fred- erking and Nirenburg, 1994). Recently, confusion network decoding for MT system combination has been proposed (Bangalore et al., 2001). To generate confusion networks, hypotheses have to be aligned against each other. In (Bangalore et al., 2001), Lev- enshtein alignment was used to generate the net- work. As opposed to speech recognition, the word order between two correct MT outputs may be dif- ferent and the Levenshtein alignment may not be able to align shifted words in the hypotheses. In (Matusov et al., 2006), different word orderings are taken into account by training alignment models by considering all hypothesis pairs as a parallel corpus using GIZA++ (Och and Ney, 2003). The size of the test set may influence the quality of these align- ments. Thus, system outputs from development sets may have to be added to improve the GIZA++ align- ments. A modified Levenshtein alignment allowing shifts as in …

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