Collaborative Research: Structure Alignment-based Machine Translation

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Researchers at New York University, Monmouth University and theUniversity of Colorado are constructing Japanese/English andChinese/English machine translation systems which automatically acquirerules from ``deep'' linguistic analyses of parallel text. This workis a natural culmination of automated example-based MachineTranslation (MT) projects that have become increasingly sophisticatedover the last two decades. The following recent advances in NaturalLanguage Processing (NLP) technologies make this inquiry feasible: (1)annotated data including bilingual treebanks and processors trained onthis data (parsers, PropBankers, etc.); (2) semantic post-processorsof parser output; (3) programs that automatically align bitexts; and(4) bilingual tree to tree translation models.Natural languages vary widely in the ordering of corresponding wordsfor equivalent expressions across linguistic boundaries and within asingle language. This research investigates ways to minimize thevariations within a single language using a type of semanticrepresentation (GLARF) that is derived automatically from syntactictrees. Such semantic representation provides for: (1) a reduction in thenumber of ways of representing the same underlying message, and (2)a way to handle long distance dependencies (e.g. relativeclauses) as local phenomena. Therefore, there is no need to resort toarbitrarily long sentence fragments or large trees fortraining. Furthermore, since less data is needed, itminimizes the sparse data problem.In the training of this translation model, because of (1), the numberof mapping rules between the source tree and the target tree isreduced. The translation model, then, is a tree transducer, with``deep'' linguistically analyzed trees for both source and targetrepresentations. In order to provide efficient computer algorithmsfor such partial mappings, this research needs to focus on(a) the training algorithm and the (b) the constraints over themapping rules in order to reduce the computational complexity.This research is expected to yield several advantages: The corearchitecture of this transducer using ``deep'' linguistic analysesshould yield more accurate results. The GLARF architecture allowscontrol over different granularity of automatically-obtainedlinguistic analyses.Broader Impact: The demand for machine translation spans from thelocal government (e.g. police forces) to national government(e.g. CIA) and the private sector. Given the growth of the Internetoutside the English speaking world, better machine translation is ofcritical importance for the broader community. This work directlyaffects the ability of English speakers to understand websites writtenin Chinese and Japanese, two of the most widely used languages on theInternet. The technique is generalizable to other language pairs andcan ultimately have even wider impact.