Stochastic Inversion Transduction Grammars for Obtaining Word Phrases for Phrase-based Statistical Machine Translation

Proceedings of the Workshop on Statistical Machine Translation, pages 130–133, New York City, June 2006. c⃝2006 Association for Computational Linguistics Stochastic Inversion Transduction Grammars for Obtaining Word Phrases for Phrase-based Statistical Machine Translation J.A. Sánchez and J.M. Benedí Departamento de Sistemas Informáticos y Computación Universidad Politécnica de Valencia Valencia, Spain jandreu@dsic.upv.es jbenedi@dsic.upv.es Abstract An important problem that is related to phrase-based statistical translation mod- els is the obtaining of word phrases from an aligned bilingual training corpus. In this work, we propose obtaining word phrases by means of a Stochastic Inver- sion Translation Grammar. Experiments on the shared task proposed in this work- shop with the Europarl corpus have been carried out and good results have been ob- tained. 1 Introduction Phrase-based statistical translation systems are cur- rently providing excellent results in real machine translation tasks (Zens et al., 2002; Och and Ney, 2003; Koehn, 2004). In phrase-based statistical translation systems, the basic translation units are word phrases. An important problem that is related to phrase- based statistical translation is to automatically ob- tain bilingual word phrases from parallel corpora. Several methods have been defined for dealing with this problem (Och and Ney, 2003). In this work, we study a method for obtaining word phrases that is based on Stochastic Inversion Transduction Gram- mars that was proposed in (Wu, 1997). Stochastic Inversion Transduction Grammars (SITG) can be viewed as a restricted Stochas- tic Context-Free Syntax-Directed Transduction Scheme. SITGs can be used to carry out a simulta- neous parsing of both the input string and the output string. In this work, we apply this idea to obtain aligned word phrases to be used in phrase-based translation systems (Sánchez and Benedí, 2006). In Section 2, we review the phrase-based machine translation approach. SITGs are reviewed in Sec- tion 3. In Section 4, we present experiments on the shared task proposed in this workshop with the Eu- roparl corpus. 2 Phrase-based Statistical Machine Transduction The translation units in a phrase-based statistical translation system are bilingual phrases rather than simple paired words. Several systems that fol- low this approach have been presented in recent works (Zens et al., 2002; Koehn, 2004). These sys- tems have demonstrated excellent translation perfor- mance in real tasks. The basic idea of a phrase-based statistical ma- chine translation system consists of the following steps (Zens et al., 2002): first, the source sentence is segmented into phrases; second, each source phrase is translated into a target phrase; and third, the target phrases are reordered in order to compose the target sentence. Bilingual translation phrases are an important component of a phrase-based system. Different methods have been defined to obtain bilingual trans- lations phrases, mainly from word-based alignments and from syntax-based models (Yamada and Knight, 2001). In this work, we focus on learning bilingual word phrases by using Stochastic Inversion Transduction Grammars (SITGs) (Wu, 1997). This formalism al- 130 lows us to obtain bilingual word phrases in a natu- ral way from the bilingual parsing of two sentences. In addition, the …

Content truncated to 500 words