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Active Semi-Supervised Learning for Improving Word Alignment ...
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Active Semi-Supervised Learning for Improving Word Alignment ...
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Active Learning and Crowdsourcing for Machine Translation in Low Resource Scenarios
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In: http://www.lti.cs.cmu.edu/research/thesis/2011/vamshi_ambati.pdf (2012)
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Active Learning for Machine Translation in Low Resource Scenarios
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In: http://www.cs.cmu.edu/afs/.cs.cmu.edu/Web/copetas/Posters/LTIProposal-Ambati10.pdf (2010)
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Active semi-supervised learning for improving word alignment
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In: http://www.cs.cmu.edu/%7Evamshi/publications/alnlp_naacl.pdf (2010)
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Extraction of Syntactic Translation Models from Parallel Data using Syntax from Source and Target Languages
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In: http://www.cs.cmu.edu/%7Ejgc/publication/Extraction_of_Syntactic_Translation_Models_from_Parallel_Data_using_Syntax_from_Source_and_Target_Languages_2009.pdf (2009)
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Linguistic Structure and Bilingual Informants Help Induce Machine Translation of Lesser-Resourced Languages ...
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Proactive Learning for Building Machine Translation Systems for Minority Languages ...
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Proactive Learning for Building Machine Translation Systems for Minority Languages ...
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Linguistic Structure and Bilingual Informants Help Induce Machine Translation of Lesser-Resourced Languages ...
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Extraction of Syntactic Translation Models from Parallel Data using Syntax from Source and Target Languages ...
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Abstract:
We propose a generic rule induction framework that is informed by syntax from both sides of a parsed parallel corpus, as sets of structural, boundary and labeling related constraints. Factoring syntax in this manner empowers our framework to work with independent annotations coming from multiple resources and not necessarily a single syntactic structure. We then explore the issue of lexical coverage of translation models learned in different scenarios using syntax from one side vs. both sides. We specifically look at how the non-isomorphic nature of parse trees for the two languages affects coverage. We propose a novel technique for restructuring targetside parse trees, that generates alternate isomorphic target trees that preserve the syntactic boundaries of constituents that were aligned in the original parse trees. We also show that combining rules extracted by restructuring syntactic trees on both sides produces significantly better translation models. The improved precision and coverage of our syntax ...
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Keyword:
80399 Computer Software not elsewhere classified; FOS Computer and information sciences
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URL: https://kilthub.cmu.edu/articles/Extraction_of_Syntactic_Translation_Models_from_Parallel_Data_using_Syntax_from_Source_and_Target_Languages/6622217 https://dx.doi.org/10.1184/r1/6622217
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Extraction of Syntactic Translation Models from Parallel Data using Syntax from Source and Target Languages ...
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Improving Syntax-Driven Translation Models by Re-structuring Divergent and Nonisomorphic Parse Tree Structures
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In: http://www.mt-archive.info/AMTA-2008-Ambati.pdf (2008)
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Improving Syntax-Driven Translation Models by Re-structuring Divergent and Nonisomorphic Parse Tree Structures
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In: http://www.cs.cmu.edu/afs/cs.cmu.edu/project/cmt-40/Nice/Papers/AMTA-08/amta.pdf (2008)
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Linguistic structure and bilingual informants help induce machine translation of lesser-resourced languages
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In: http://www.mt-archive.info/LREC-2008-Monson.pdf (2008)
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Linguistic structure and bilingual informants help induce machine translation of lesser-resourced languages
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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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Linguistic structure and bilingual informants help induce machine translation of lesser-resourced languages
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Linguistic Structure and Bilingual Informants to Induce Machine Translation of Lesser-Resourced Languages ...
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Linguistic Structure and Bilingual Informants to Induce Machine Translation of Lesser-Resourced Languages ...
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A hybrid approach to example based machine translation for Indian languages
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In: http://www.mt-archive.info/ICON-2007-Ambati.pdf (2007)
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