Statistical Machine Translation Using Coercive Two-Level Syntactic Transduction

Statistical Machine Translation Using Coercive Two-Level Syntactic Transduction Charles Schafer and David Yarowsky Center for Language and Speech Processing / Department of Computer Science Johns Hopkins University Baltimore, MD 21218 USA {cschafer,yarowsky}@cs.jhu.edu Abstract We define, implement and evaluate a novel model for statistical machine translation, which is based on shal- low syntactic analysis (part-of-speechtagging and phrase chunking) in both the source and target languages. It is able to model long-distance constituent motion and other syntactic phenomena without requiring a full parse in either language. We also examine aspects of lexical transfer, suggesting and exploring a concept of transla- tion coercion across parts of speech, as well as a transfer model based on lemma-to-lemma translation probabili- ties, which holds promise for improving machine trans- lation of low-density languages. Experiments are per- formed in both Arabic-to-English and French-to-English translation demonstrating the efficacy of the proposed techniques. Performance is automatically evaluated via the Bleu score metric. 1 Introduction In this work we define, implement and evaluate a novel model for statistical machine translation (SMT). Our goal was to produce a SMT system for translat- ing foreign languages into English which utilizes some syntactic information in both the foreign language and English without, however, requiring a full parse in either language. Some advantages of not relying on full parses include that (1) there is a lack of availability of parsers for many languages of interest; (2) parsing time com- plexity represents a potential bottleneck for both model training and testing. Intuitively, the explicit modeling of syntactic phenom- ena should be of benefit in the machine translation task; the ability to handle long-distance motion in an intelli- gently constrained way is a salient example of such a benefit. Allowing unconstrained translation reorderings at the word level generates a very large set of permu- tations that pose a difficult search problem at decoding time. We propose a model that makes use of shallow parses (text chunking) to support long-distance motion of phrases without requiring deeper analysis of syntax. The resources required to train this system on a new lan- guage are minimal, and we gain the ability to model long-distance movement and some interesting proper- ties of lexical translation across parts of speech. One of the source languages we examine in this paper, Arabic, has a canonical sentence-level order of Verb-Subject- Object, which means that translation into English (with a standard ordering of Subject-Verb-Object) commonly requires motion of entire phrasal constituents, which is not true of French-to-English translation, to cite one lan- guage pair whose characteristics have wielded great in- fluence in the history of work on statistical machine translation. A key motivation for and objective of this work was to build a translation model and feature space to handle the above-described phenomenon effectively. 2 Prior Work Statistical machine translation, as pioneered by IBM (e.g. Brown et al., 1993), is grounded in the noisy chan- nel model. And similar to the related channel problems of speech and handwriting recognition, the original SMT language pair French-English exhibits …

Content truncated to 500 words