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Handling Translation Divergences: Combining Statistical and Symbolic Techniques in Generation-Heavy Machine Translation

dc.contributor.authorHabash, Nizaren_US
dc.contributor.authorDorr, Bonnieen_US
dc.description.abstractThis paper describes a novel approach to handling translation divergences in a Generation-Heavy Hybrid Machine Translation (GHMT) system.The translation divergence problem is usually reserved for Transfer and Interlingual MT because it requires a large combination of complex lexical and structural mappings. A major requirement of these approaches is the accessibility of large amounts of explicit symmetrical knowledge for both source and target languages. This limitation renders Transfer and Interlingual approaches ineffective in the face of structurally-divergent language pairs with asymmetrical resources. GHMT addresses the more common form of this problem, ource-poor/target-rich, by fully exploiting symbolic and statistical target-language resources. This is accomplished by using target-language lexical semantics, categorial variations and subcategorization frames to overgenerate multiple lexico-structural variations from a target-glossed syntactic dependency of the source-language sentence. The symbolic overgeneration, which accounts for different possible translation divergences, is constrained by a statistical target-language model. (Also LAMP-TR-088) (Also UMIACS-TR-2002-49)en_US
dc.format.extent191897 bytes
dc.relation.ispartofseriesUM Computer Science Department; CS-TR-4369en_US
dc.relation.ispartofseriesUMIACS; UMIACS-TR-2002-49en_US
dc.titleHandling Translation Divergences: Combining Statistical and Symbolic Techniques in Generation-Heavy Machine Translationen_US
dc.typeTechnical Reporten_US
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_US
dc.relation.isAvailableAtUniversity of Maryland (College Park, Md.)en_US
dc.relation.isAvailableAtTech Reports in Computer Science and Engineeringen_US
dc.relation.isAvailableAtUMIACS Technical Reportsen_US

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