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dc.contributor.authorLevin, Lori
dc.contributor.authorAlvarez, Alison
dc.contributor.authorFrederking, Robert
dc.contributor.authorGood, Jeff
dc.date.accessioned2015-10-10T12:00:56Z
dc.date.available2015-10-10T12:00:56Z
dc.date.issued2008
dc.identifier.citationIn Annie Zaenen, Jane Simpson, Tracy Holloway King, Jane Grimshaw, Joan Maling, and Chris Manning (eds.), Architectures, rules, and preferences: Variations on themes by Joan W. Bresnan. Stanford: CSLI. 253–275.en_US
dc.identifier.urihttp://hdl.handle.net/10477/38496
dc.description.abstractAvenue (Probst et al., 2002, Monson et al., 2004, Lavie et al., 2003, Font-Llitjoset al.,2005)1 is a machine translation system that automatically learns translation rules between two languages. In the Avenue scenario, one of the languages is a resource rich language like English or Spanish, for which there are many human and electronic resources (corpora,morphological analyzers, lexica, etc.).The other is a resource poor language with few human and electronic resources.For example, there might be no linguist available to write translation rules and there might not be large enough corpora for automatic machine learning of translation rules. This is true for the vast majority of languages. Within the current state of the art in commercial machine translation, it is not possible to build machine translation (MT) systems for resource poor languages.However,we have met with many indigenous communities (Mapuche, Quechua, and others), who want their languages to be used in jobs, education, government, and health care. Machine translation can be a tool for maintaining functionality in their languages, because it can help them access the content of the Internet and disseminate local culture and information without having to adopt a major national language like Spanish or English. The vision of the Avenue project is equal access to information for speakers of all languages.en_US
dc.language.isoen_USen_US
dc.publisherCSLIen_US
dc.subjectmachine translationen_US
dc.subjectfeature detectionen_US
dc.subjectAVENUE systemen_US
dc.subjectcorpus linguisticsen_US
dc.titleAutomatic Learning of Grammatical Encodingen_US
dc.typeBook chapteren_US


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