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1
Abstract Meaning Representation (AMR) Annotation Release 3.0
Knight, Kevin; Badarau, Bianca; Baranescu, Laura. - : Linguistic Data Consortium, 2020. : https://www.ldc.upenn.edu, 2020
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2
Abstract Meaning Representation (AMR) Annotation Release 3.0 ...
Knight, Kevin; Badarau, Bianca; Baranescu, Laura. - : Linguistic Data Consortium, 2020
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3
Abstract Meaning Representation (AMR) Annotation Release 2.0
Knight, Kevin; Badarau, Bianca; Baranescu, Laura. - : Linguistic Data Consortium, 2017. : https://www.ldc.upenn.edu, 2017
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4
Abstract Meaning Representation (AMR) Annotation Release 2.0 ...
Knight, Kevin; Badarau, Bianca; Baranescu, Laura. - : Linguistic Data Consortium, 2017
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5
Abstract Meaning Representation (AMR) Annotation Release 1.0
Knight, Kevin; Baranescu, Laura; Bonial, Claire. - : Linguistic Data Consortium, 2014. : https://www.ldc.upenn.edu, 2014
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6
Abstract Meaning Representation (AMR) Annotation Release 1.0 ...
Palmer, Martha; Marcu, Daniel; Griffitt, Kira. - : Linguistic Data Consortium, 2014
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7
Scalable Inference and Training of Context-Rich Syntactic Translation Models
Graehl, Jonathan; Galley, Michel; DeNeefe, Steve. - : Proceedings of COLING/ACL, 2006
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8
Scalable Inference and Training of Context-Rich Syntactic Translation Models ...
Galley, Michel; Graehl, Jonathan; Knight, Kevin. - : Columbia University, 2006
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9
What's in a Translation Rule
In: DTIC (2004)
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10
Syntax-based Alignment of Multiple Translations: Extracting Paraphrases and Generating New Sentences
In: DTIC (2003)
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11
Translation with scarce bilingual resources
In: Machine translation. - Dordrecht [u.a.] : Springer Science + Business Media 17 (2002) 1, 1-17
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12
Translation with Scarce Bilingual Resources
In: Machine translation. - Dordrecht [u.a.] : Springer Science + Business Media 17 (2002) 1, 1-18
OLC Linguistik
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13
The Importance of Lexicalized Syntax Models for Natural Language Generation Tasks
In: DTIC (2002)
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14
Fast Decoding and Optimal Decoding for Machine Translation
In: DTIC (2001)
Abstract: A good decoding algorithm is critical to the success of any statistical machine translation system. The decoder's job is to find the translation that is most likely according to set of previously learned parameters (and a formula for combining them). Since the space of possible translations is extremely large, typical decoding algorithms are only able to examine a portion of it, thus risking to miss good solutions. In this paper, we compare the speed and output quality of a traditional stack-based decoding algorithm with two new decoders: a fast greedy decoder and a slow but optimal decoder that treats decoding as an integer-programming optimization problem.
Keyword: *ALGORITHMS; *DECODING; *MACHINE TRANSLATION; DECODERS; JOBS; Linguistics; Numerical Mathematics; OPTIMIZATION; OUTPUT; QUALITY; STATISTICS; TRANSLATIONS
URL: http://www.dtic.mil/docs/citations/ADA459945
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA459945
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