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    EMNLP
    2009
    166views Natural Language Processing» more  EMNLP 2009»
    Weighted Alignment Matrices for Statistical Machine Translation
    13 years 5 months ago
    Weighted Alignment Matrices for Statistical Machine Translation
    Download  mtgroup.ict.ac.cn
    Current statistical machine translation systems usually extract rules from bilingual corpora annotated with 1-best alignments. They are prone to learn noisy rules due to alignment...
    Yang Liu, Tian Xia, Xinyan Xiao, Qun Liu
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    ACL
    2009
    133views Computational Linguistics» more  ACL 2009»
    Better Word Alignments with Supervised ITG Models
    13 years 5 months ago
    Better Word Alignments with Supervised ITG Models
    Download  john.blitzer.com
    This work investigates supervised word alignment methods that exploit inversion transduction grammar (ITG) constraints. We consider maximum margin and conditional likelihood objec...
    Aria Haghighi, John Blitzer, John DeNero, Dan Klei...
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    ACL
    2006
    107views Computational Linguistics» more  ACL 2006»
    BiTAM: Bilingual Topic AdMixture Models for Word Alignment
    13 years 9 months ago
    BiTAM: Bilingual Topic AdMixture Models for Word Alignment
    Download  www.sailing.cs.cmu.edu
    We propose a novel bilingual topical admixture (BiTAM) formalism for word alignment in statistical machine translation. Under this formalism, the parallel sentence-pairs within a ...
    Bing Zhao, Eric P. Xing
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    ACL
    1998
    99views Computational Linguistics» more  ACL 1998»
    A Stochastic Language Model using Dependency and Its Improvement by Word Clustering
    13 years 9 months ago
    A Stochastic Language Model using Dependency and Its Improvement by Word Clustering
    Download  www.aclweb.org
    In this paper, we present a stochastic language model for Japanese using dependency. The prediction unit in thismodel isallattributeof "bunsetsu". This isrepresented by ...
    Shinsuke Mori, Makoto Nagao
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    NAACL
    2007
    84views Computational Linguistics» more  NAACL 2007»
    Applying Many-to-Many Alignments and Hidden Markov Models to Letter-to-Phoneme Conversion
    13 years 9 months ago
    Applying Many-to-Many Alignments and Hidden Markov Models to Letter-to-Phoneme Conversion
    Download  acl.ldc.upenn.edu
    Letter-to-phoneme conversion generally requires aligned training data of letters and phonemes. Typically, the alignments are limited to one-to-one alignments. We present a novel t...
    Sittichai Jiampojamarn, Grzegorz Kondrak, Tarek Sh...
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