Clause Restructuring for Statistical Machine Translation

Proceedings of the 43rd Annual Meeting of the ACL, pages 531–540, Ann Arbor, June 2005. c⃝2005 Association for Computational Linguistics Clause Restructuring for Statistical Machine Translation Michael Collins MIT CSAIL mcollins@csail.mit.edu Philipp Koehn School of Informatics University of Edinburgh pkoehn@inf.ed.ac.uk Ivona Kuˇcerov´a MIT Linguistics Department kucerova@mit.edu Abstract We describe a method for incorporating syntactic informa- tion in statistical machine translation systems. The first step of the method is to parse the source language string that is be- ing translated. The second step is to apply a series of trans- formations to the parse tree, effectively reordering the surface string on the source language side of the translation system. The goal of this step is to recover an underlying word order that is closer to the target language word-order than the original string. The reordering approach is applied as a pre-processing step in both the training and decoding phases of a phrase-based statis- tical MT system. We describe experiments on translation from German to English, showing an improvement from 25.2% Bleu score for a baseline system to 26.8% Bleu score for the system with reordering, a statistically significant improvement. 1 Introduction Recent research on statistical machine translation (SMT) has lead to the development of phrase- based systems (Och et al., 1999; Marcu and Wong, 2002; Koehn et al., 2003). These methods go be- yond the original IBM machine translation models (Brown et al., 1993), by allowing multi-word units (“phrases”) in one language to be translated directly into phrases in another language. A number of em- pirical evaluations have suggested that phrase-based systems currently represent the state–of–the–art in statistical machine translation. In spite of their success, a key limitation of phrase-based systems is that they make little or no direct use of syntactic information. It appears likely that syntactic information will be crucial in accu- rately modeling many phenomena during transla- tion, for example systematic differences between the word order of different languages. For this reason there is currently a great deal of interest in meth- ods which incorporate syntactic information within statistical machine translation systems (e.g., see (Al- shawi, 1996; Wu, 1997; Yamada and Knight, 2001; Gildea, 2003; Melamed, 2004; Graehl and Knight, 2004; Och et al., 2004; Xia and McCord, 2004)). In this paper we describe an approach for the use of syntactic information within phrase-based SMT systems. The approach constitutes a simple, direct method for the incorporation of syntactic informa- tion in a phrase–based system, which we will show leads to signifi cant improvements in translation ac- curacy. The fi rst step of the method is to parse the source language string that is being translated. The second step is to apply a series of transformations to the resulting parse tree, effectively reordering the surface string on the source language side of the translation system. The goal of this step is to re- cover an underlying word order that is closer to the target language word-order than the original string. Finally, we apply a phrase-based system to the re- …

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