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IBM alignment models
IBM alignment models are a sequence of increasingly complex models used in statistical machine translation to train a translation model and an…
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Related topics
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3 relations
Hidden Markov model
Statistical machine translation
Broader (1)
Machine translation
Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
2011
2011
Unsupervised Concept Annotation using Latent Dirichlet Allocation and Segmental Methods
Nathalie Camelin
,
Boris Detienne
,
Stéphane Huet
,
Dominique Quadri
,
F. Lefèvre
ULNLP@EMNLP
2011
Corpus ID: 3011968
Training efficient statistical approaches for natural language understanding generally requires data with segmental semantic…
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2010
2010
Refining Word Alignment with Discriminative Training
Nadi Tomeh
,
A. Allauzen
,
Guillaume Wisniewski
,
François Yvon
Conference of the Association for Machine…
2010
Corpus ID: 9033216
The quality of statistical machine translation systems depends on the quality of the word alignments that are computed during the…
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2010
2010
It Depends on the Translation: Unsupervised Dependency Parsing via Word Alignment
Samuel Brody
Conference on Empirical Methods in Natural…
2010
Corpus ID: 6568443
We reveal a previously unnoticed connection between dependency parsing and statistical machine translation (SMT), by formulating…
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2005
2005
Maximum Entropy Modeling: A Suitable Framework to Learn Context-Dependent Lexicon Models for Statistical Machine Translation
I. García-Varea
,
F. Casacuberta
Machine-mediated learning
2005
Corpus ID: 6602861
Current statistical machine translation systems are mainly based on statistical word lexicons. However, these models are usually…
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