Phrase-Based SMT with Shallow Tree-Phrases

Proceedings of the Workshop on Statistical Machine Translation, pages 39–46, New York City, June 2006. c⃝2006 Association for Computational Linguistics Phrase-Based SMT with Shallow Tree-Phrases Philippe Langlais and Fabrizio Gotti RALI – DIRO Universit´e de Montr´eal, C.P. 6128 Succ. Centre-Ville H3C 3J7, Montr´eal, Canada {felipe,gottif}@iro.umontreal.ca Abstract In this article, we present a translation system which builds translations by glu- ing together Tree-Phrases, i.e. associ- ations between simple syntactic depen- dency treelets in a source language and their corresponding phrases in a target language. The Tree-Phrases we use in this study are syntactically informed and present the advantage of gathering source and target material whose words do not have to be adjacent. We show that the phrase-based translation engine we imple- mented benefits from Tree-Phrases. 1 Introduction Phrase-based machine translation is now a popular paradigm. It has the advantage of naturally cap- turing local reorderings and is shown to outper- form word-based machine translation (Koehn et al., 2003). The underlying unit (a pair of phrases), how- ever, does not handle well languages with very dif- ferent word orders and fails to derive generalizations from the training corpus. Several alternatives have been recently proposed to tackle some of these weaknesses. (Matusov et al., 2005) propose to reorder the source text in or- der to mimic the target word order, and then let a phrase-based model do what it is good at. (Simard et al., 2005) detail an approach where the standard phrases are extended to account for “gaps” either on the target or source side. They show that this repre- sentation has the potential to better exploit the train- ing corpus and to nicely handle differences such as negations in French and English that are poorly han- dled by standard phrase-based models. Others are considering translation as a syn- chronous parsing process e.g. (Melamed, 2004; Ding and Palmer, 2005)) and several algorithms have been proposed to learn the underlying produc- tion rule probabilities (Graehl and Knight, 2004; Ding and Palmer, 2004). (Chiang, 2005) proposes an heuristic way of acquiring context free transfer rules that significantly improves upon a standard phrase-based model. As mentioned in (Ding and Palmer, 2005), most of these approaches require some assumptions on the level of isomorphism (lexical and/or structural) between two languages. In this work, we consider a simple kind of unit: a Tree-Phrase (TP), a com- bination of a fully lexicalized treelet (TL) and an elastic phrase (EP), the tokens of which may be in non-contiguous positions. TPs capture some syntac- tic information between two languages and can eas- ily be merged with standard phrase-based engines. A TP can be seen as a simplification of the treelet pairs manipulated in (Quirk et al., 2005). In particu- lar, we do not address the issue of projecting a source treelet into a target one, but take the bet that collect- ing (without structure) the target words associated with the words encoded in the nodes of a treelet will suffice to allow translation. This set of target words is what …

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