Dependency Treelet Translation: Syntactically Informed Phrasal SMT

Proceedings of the 43rd Annual Meeting of the ACL, pages 271–279, Ann Arbor, June 2005. c⃝2005 Association for Computational Linguistics Dependency Treelet Translation: Syntactically Informed Phrasal SMT Chris Quirk, Arul Menezes Colin Cherry Microsoft Research University of Alberta One Microsoft Way Edmonton, Alberta Redmond, WA 98052 Canada T6G 2E1 {chrisq,arulm}@microsoft.com colinc@cs.ualberta.ca Abstract We describe a novel approach to statistical machine translation that combines syntactic information in the source language with recent advances in phrasal translation. This method requires a source-language dependency parser, target language word segmentation and an unsupervised word alignment component. We align a parallel corpus, project the source dependency parse onto the target sentence, extract dependency treelet translation pairs, and train a tree-based ordering model. We describe an efficient decoder and show that using these tree- based models in combination with conventional SMT models provides a promising approach that incorporates the power of phrasal SMT with the linguistic generality available in a parser. 1. Introduction Over the past decade, we have witnessed a revolution in the field of machine translation (MT) toward statistical or corpus-based methods. Yet despite this success, statistical machine translation (SMT) has many hurdles to overcome. While it excels at translating domain-specific terminology and fixed phrases, grammatical generalizations are poorly captured and often mangled during translation (Thurmair, 04). 1.1. Limitations of string-based phrasal SMT State-of-the-art phrasal SMT systems such as (Koehn et al., 03) and (Vogel et al., 03) model translations of phrases (here, strings of adjacent words, not syntactic constituents) rather than individual words. Arbitrary reordering of words is allowed within memorized phrases, but typically only a small amount of phrase reordering is allowed, modeled in terms of offset positions at the string level. This reordering model is very limited in terms of linguistic generalizations. For instance, when translating English to Japanese, an ideal system would automatically learn large- scale typological differences: English SVO clauses generally become Japanese SOV clauses, English post-modifying prepositional phrases become Japanese pre-modifying postpositional phrases, etc. A phrasal SMT system may learn the internal reordering of specific common phrases, but it cannot generalize to unseen phrases that share the same linguistic structure. In addition, these systems are limited to phrases contiguous in both source and target, and thus cannot learn the generalization that English not may translate as French ne…pas except in the context of specific intervening words. 1.2. Previous work on syntactic SMT1 The hope in the SMT community has been that the incorporation of syntax would address these issues, but that promise has yet to be realized. One simple means of incorporating syntax into SMT is by re-ranking the n-best list of a baseline SMT system using various syntactic models, but Och et al. (04) found very little positive impact with this approach. However, an n-best list of even 16,000 translations captures only a tiny fraction of the ordering possibilities of a 20 word sentence; re-ranking provides the syntactic model no opportunity to boost or prune large sections of that search space. Inversion Transduction Grammars (Wu, 97), or …

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