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Document Type : Latin Dissertation
Language of Document : English
Record Number : 148457
Doc. No : ET20249
Main Entry : KATHARINA PROBST
Title Proper : Learning Transfer Rules for Machine Translation with Limited Data
Note : This document is digital این مدرک بصورت الکترونیکی می باشد
Abstract : The transfer-based approach to machine translation (MT) captures struc-tural transfers between the source language and the target language, withthe goal of producing grammatical translations. The major drawback ofthe approach is the development bottleneck, requiring many human-yearsof rule development. On the other hand, data-driven approaches such asexample-based and statistical MT achieve fast system development by de-riving mostly non-structural translation information from bilingual corpora.This thesis aims at striking a balance between both approaches by inferringtransfer rules automatically from bilingual text, aiming specifically at sce-narios where bilingual data is in sparse supply. The rules are learned usinga variety of information, such as parses that are available for one of the lan-guages, and morphological information that is available for both languages.They are learned in three stages, first.
Subject : Electericl tess
: برق
electronic file name : TL43375.pdf
Title and statement of responsibility and : Learning Transfer Rules for Machine Translation with Limited Data [Thesis]
 
 
 
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