Deep open-source machine translation

Authors:
Francis Bond;Stephan Oepen;Eric Nichols;Dan Flickinger;Erik Velldal;Petter Haugereid
Affiliations:
Division of Linguistics and Multilingual Studies, Nanyang Technological University, Singapore, Singapore;Department of Informatics, University of Oslo, Oslo, Norway;Graduate School of Information Sciences, Tohoku University, Sendai, Japan;Center for the Study of Language and Information, Stanford University, Stanford, USA;Department of Informatics, University of Oslo, Oslo, Norway;Division of Linguistics and Multilingual Studies, Nanyang Technological University, Singapore, Singapore
Venue:
Machine Translation
Year:
2011

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Abstract

This paper summarizes ongoing efforts to provide software infrastructure (and methodology) for open-source machine translation that combines a deep semantic transfer approach with advanced stochastic models. The resulting infrastructure combines precise grammars for parsing and generation, a semantic-transfer based translation engine and stochastic controllers. We provide both a qualitative and quantitative experience report from instantiating our general architecture for Japanese---English MT using only open-source components, including HPSG-based grammars of English and Japanese.