Statistical Machine Translation through Global Lexical Selection and Sentence Reconstruction

Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 152–159, Prague, Czech Republic, June 2007. c⃝2007 Association for Computational Linguistics Statistical Machine Translation through Global Lexical Selection and Sentence Reconstruction Srinivas Bangalore, Patrick Haffner, Stephan Kanthak AT&T Labs - Research 180 Park Ave, Florham Park, NJ 07932 {srini,haffner,skanthak}@research.att.com Abstract Machine translation of a source language sentence involves selecting appropriate tar- get language words and ordering the se- lected words to form a well-formed tar- get language sentence. Most of the pre- vious work on statistical machine transla- tion relies on (local) associations of target words/phrases with source words/phrases for lexical selection. In contrast, in this pa- per, we present a novel approach to lexical selection where the target words are associ- ated with the entire source sentence (global) without the need to compute local associa- tions. Further, we present a technique for reconstructing the target language sentence from the selected words. We compare the re- sults of this approach against those obtained from a finite-state based statistical machine translation system which relies on local lex- ical associations. 1 Introduction Machine translation can be viewed as consisting of two subproblems: (a) lexical selection, where appro- priate target language lexical items are chosen for each source language lexical item and (b) lexical re- ordering, where the chosen target language lexical items are rearranged to produce a meaningful target language string. Most of the previous work on statis- tical machine translation, as exemplified in (Brown et al., 1993), employs word-alignment algorithm (such as GIZA++ (Och and Ney, 2003)) that pro- vides local associations between source and target words. The source-to-target word alignments are sometimes augmented with target-to-source word alignments in order to improve precision. Further, the word-level alignments are extended to phrase- level alignments in order to increase the extent of local associations. The phrasal associations compile some amount of (local) lexical reordering of the tar- get words – those permitted by the size of the phrase. Most of the state-of-the-art machine translation sys- tems use phrase-level associations in conjunction with a target language model to produce sentences. There is relatively little emphasis on (global) lexical reordering other than the local reorderings permit- ted within the phrasal alignments. A few exceptions are the hierarchical (possibly syntax-based) trans- duction models (Wu, 1997; Alshawi et al., 1998; Yamada and Knight, 2001; Chiang, 2005) and the string transduction models (Kanthak et al., 2005). In this paper, we present an alternate approach to lexical selection and lexical reordering. For lexical selection, in contrast to the local approaches of as- sociating target to source words, we associate tar- get words to the entire source sentence. The intu- ition is that there may be lexico-syntactic features of the source sentence (not necessarily a single source word) that might trigger the presence of a target word in the target sentence. Furthermore, it might be difficult to exactly associate a target word to a source word in many situations – (a) when the translations are not exact …

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