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1
Linguistic structure and bilingual informants help induce machine translation of lesser-resourced languages
In: http://www.mt-archive.info/LREC-2008-Monson.pdf (2008)
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2
Linguistic structure and bilingual informants help induce machine translation of lesser-resourced languages
In: http://www.cs.cmu.edu/afs/cs.cmu.edu/project/cmt-40/Nice/Papers/lrec-2008/LeveragingLinguisticStructureToLearnMTOfLesserResourcedLanguages/LeveragingLinguisticStructureToLearnMTOfLesserResourcedLanguages_v14.pdf (2008)
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3
Linguistic structure and bilingual informants help induce machine translation of lesser-resourced languages
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4
A Trainable Transfer-based Machine Translation Approach for Languages with Limited Resources
In: http://www.cs.cmu.edu/afs/cs.cmu.edu/user/alavie/www/papers/EAMT-XFER-Apr04.pdf (2004)
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5
Experiments with a Hindi-toEnglish transfer-based MT system under a miserly data scenario
In: http://www.mt-archive.info/TALIP-2003-Lavie.pdf (2003)
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6
Experiments with a Hindi-to-English Transfer-based MT System under a Miserly Data Scenario
In: http://sidecar.sp.cs.cmu.edu/ari/papers/TALIP-SLE-03.pdf (2003)
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7
Automatic Rule Learning for Resource-Limited MT
In: http://www-2.cs.cmu.edu/afs/cs/user/alavie/www/papers/amta02CarbonellEtAl.pdf (2002)
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8
Automatic Rule Learning for Resource-Limited MT
In: http://www-2.cs.cmu.edu/~kathrin/amta02CarbonellEtAl.ps (2002)
Abstract: Machine Translation of minority languages presents unique challenges, including the paucity of bilingual training data and the unavailability of linguistically-trained speakers. This paper focuses on a machine learning approach to transfer-based MT, where data in the form of translations and lexical alignments are elicited from bilingual speakers, and a seeded version-space learning algorithm formulates and re nes transfer rules. A rule-generalization lattice is de ned based on LFG-style f-structures, permitting generalization operators in the search for the most general rules consistent with the elicited data. The paper presents these methods and illustrates examples.
URL: http://www-2.cs.cmu.edu/~kathrin/amta02CarbonellEtAl.ps
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.3080
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9
Design and Implementation of Controlled Elicitation for Machine Translation of
In: http://www.eamt.org/summitVIII/./papers/probst.pdf (2001)
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10
Design and Implementation of Controlled Elicitation for Machine Translation of Low-density Languages
In: http://www.cs.cmu.edu/~kathrin/mts01ProbstEtAl.ps (2001)
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11
Design and Implementation of Controlled Elicitation for Machine Translation of Low-Density Languages
In: http://www-2.cs.cmu.edu/afs/cs.cmu.edu/user/alavie/www/papers/MTSummit-01-kathrin.pdf (2001)
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12
Linguistic Structure and Bilingual Informants Help Induce Machine Translation of Lesser-Resourced Languages
In: http://www.lrec-conf.org/proceedings/lrec2008/pdf/725_paper.pdf
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13
Linguistic Structure and Bilingual Informants Help Induce Machine Translation of Lesser-Resourced Languages
In: http://www.cs.cmu.edu/afs/cs/user/alavie/www/papers/LeveragingLinguisticStructureToLearnMTOfLesserResourcedLanguages_v11.pdf
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14
Design and Implementation of Controlled Elicitation for Machine Translation of Low-density Languages
In: http://www.elsnet.org/mt2010/probst.pdf
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15
Design and Implementation of Controlled Elicitation for Machine Translation of Low-density Languages
In: http://www.mt-archive.info/MTS-2001-Probst.pdf
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