CAREER: Semantics for Statistical Machine Translation

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The past few years have seen a revolution in machinetranslation, with the widespread adoption ofstatistical systems trained on large amounts ofparallel bilingual text. Recent evaluations have shownthat current statistically trained research technologysignificantly outperforms commercially available MTsystems such as those available on the web. But evenstate-of-the-art systems produce garbled translationsmore often than not. Further improvements in machinetranslation will require major changes in thearchitecture of statistical systems. Our research aimsto improve the quality of machine translation output byallowing statistical systems to handle deeper, semanticrepresentations.Our approach focuses on improving statistical machinetranslation by using a semantic representation at thelevel of predicate-argument structure. This workbuilds on the recent success in statistical approachesto shallow language understanding, and tree-basedalgorithms for machine translation using syntacticparses of the source and target sentences. Over thecourse of the project we aim to: first, develop robustsemantic parsing systems capable of generalizing to newdomains and apply them to large bilingual corpora,second, develop probabilistic models of translationthat use the resulting level of representation and canbe practically trained, and third, integrate languageunderstanding and translation to allow efficient searchfor the best overall translation of new sentences.