Tree-to-String Alignment Template for Statistical Machine Translation

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 609–616, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Tree-to-String Alignment Template for Statistical Machine Translation Yang Liu , Qun Liu , and Shouxun Lin Institute of Computing Technology Chinese Academy of Sciences No.6 Kexueyuan South Road, Haidian District P. O. Box 2704, Beijing, 100080, China {yliu,liuqun,sxlin}@ict.ac.cn Abstract We present a novel translation model based on tree-to-string alignment template (TAT) which describes the alignment be- tween a source parse tree and a target string. A TAT is capable of generating both terminals and non-terminals and per- forming reordering at both low and high levels. The model is linguistically syntax- based because TATs are extracted auto- matically from word-aligned, source side parsed parallel texts. To translate a source sentence, we first employ a parser to pro- duce a source parse tree and then ap- ply TATs to transform the tree into a tar- get string. Our experiments show that the TAT-based model significantly outper- forms Pharaoh, a state-of-the-art decoder for phrase-based models. 1 Introduction Phrase-based translation models (Marcu and Wong, 2002; Koehn et al., 2003; Och and Ney, 2004), which go beyond the original IBM trans- lation models (Brown et al., 1993) 1 by model- ing translations of phrases rather than individual words, have been suggested to be the state-of-the- art in statistical machine translation by empirical evaluations. In phrase-based models, phrases are usually strings of adjacent words instead of syntactic con- stituents, excelling at capturing local reordering and performing translations that are localized to 1The mathematical notation we use in this paper is taken from that paper: a source string f J 1 = f1, . . . , fj, . . . , fJ is to be translated into a target string eI 1 = e1, . . . , ei, . . . , eI. Here, I is the length of the target string, and J is the length of the source string. substrings that are common enough to be observed on training data. However, a key limitation of phrase-based models is that they fail to model re- ordering at the phrase level robustly. Typically, phrase reordering is modeled in terms of offset po- sitions at the word level (Koehn, 2004; Och and Ney, 2004), making little or no direct use of syn- tactic information. Recent research on statistical machine transla- tion has lead to the development of syntax-based models. Wu (1997) proposes Inversion Trans- duction Grammars, treating translation as a pro- cess of parallel parsing of the source and tar- get language via a synchronized grammar. Al- shawi et al. (2000) represent each production in parallel dependency tree as a finite transducer. Melamed (2004) formalizes machine translation problem as synchronous parsing based on multi- text grammars. Graehl and Knight (2004) describe training and decoding algorithms for both gen- eralized tree-to-tree and tree-to-string transduc- ers. Chiang (2005) presents a hierarchical phrase- based model that uses hierarchical phrase pairs, which are formally …

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