A Discriminative Syntactic Word Order Model for Machine Translation

Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 9–16, Prague, Czech Republic, June 2007. c⃝2007 Association for Computational Linguistics A Discriminative Syntactic Word Order Model for Machine Translation Pi-Chuan Chang∗ Computer Science Department Stanford University Stanford, CA 94305 pichuan@stanford.edu Kristina Toutanova Microsoft Research Redmond, WA kristout@microsoft.com Abstract We present a global discriminative statistical word order model for machine translation. Our model combines syntactic movement and surface movement information, and is discriminatively trained to choose among possible word orders. We show that com- bining discriminative training with features to detect these two different kinds of move- ment phenomena leads to substantial im- provements in word ordering performance over strong baselines. Integrating this word order model in a baseline MT system results in a 2.4 points improvement in BLEU for English to Japanese translation. 1 Introduction The machine translation task can be viewed as con- sisting of two subtasks: predicting the collection of words in a translation, and deciding the order of the predicted words. For some language pairs, such as English and Japanese, the ordering problem is es- pecially hard, because the target word order differs significantly from the source word order. Previous work has shown that it is useful to model target language order in terms of movement of syn- tactic constituents in constituency trees (Yamada and Knight, 2001; Galley et al., 2006) or depen- dency trees (Quirk et al., 2005), which are obtained using a parser trained to determine linguistic con- stituency. Alternatively, order is modelled in terms of movement of automatically induced hierarchical structure of sentences (Chiang, 2005; Wu, 1997). ∗ This research was conducted during the author’s intern- ship at Microsoft Research. The advantages of modeling how a target lan- guage syntax tree moves with respect to a source lan- guage syntax tree are that (i) we can capture the fact that constituents move as a whole and generally re- spect the phrasal cohesion constraints (Fox, 2002), and (ii) we can model broad syntactic reordering phenomena, such as subject-verb-object construc- tions translating into subject-object-verb ones, as is generally the case for English and Japanese. On the other hand, there is also significant amount of information in the surface strings of the source and target and their alignment. Many state-of-the-art SMT systems do not use trees and base the ordering decisions on surface phrases (Och and Ney, 2004; Al-Onaizan and Papineni, 2006; Kuhn et al., 2006). In this paper we develop an order model for machine translation which makes use of both syntactic and surface information. The framework for our statistical model is as fol- lows. We assume the existence of a dependency tree for the source sentence, an unordered dependency tree for the target sentence, and a word alignment between the target and source sentences. Figure 1 (a) shows an example of aligned source and target dependency trees. Our task is to order the target de- pendency tree. We train a statistical model to select the best or- der of the unordered …

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