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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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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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Abstract:
Syntax-based approaches to statistical MT require syntax-aware methods for acquiring their underlying translation models from par-allel data. This acquisition process can be driven by syntactic trees for either the source or target language, or by trees on both sides. Work to date has demonstrated that using trees for both sides suffers from severe cov-erage problems. This is primarily due to the highly restrictive space of constituent segmen-tations that the trees on two sides introduce, which adversely affects the recall of the re-sulting translation models. Approaches that project from trees on one side, on the other hand, have higher levels of recall, but suf-fer from lower precision, due to the lack of syntactically-aware word alignments. In this paper we explore the issue of lexical coverage of the translation models learned in both of these scenarios. We specifically look at how the non-isomorphic nature of the parse trees for the two languages affects recall and cov-erage. We then propose a novel technique for restructuring target parse trees, that generates highly isomorphic target trees that preserve the syntactic boundaries of constituents that were aligned in the original parse trees. We evaluate the translation models learned from these restructured trees and show that they are significantly better than those learned using trees on both sides and trees on one side. 1
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URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.557.9598 http://www.mt-archive.info/AMTA-2008-Ambati.pdf
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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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