N-Gram Language Models

All competitive statistical machine translation systems use n-gram language models that predict the probability of a word from a maximum window of proceeding words.

N Gram Language Models is the main subject of 7 publications. 6 are discussed here.

Publications

Irving J. Good (1953): The population frequency of species and the estimation of population parameters, Biometrika

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@Article{GoodTuring,
author = {Irving J. Good},
title = {The population frequency of species and the estimation of population parameters},
url = {http://www.ling.upenn.edu/courses/cogs502/GoodTuring1953.pdf},
googlescholar = {2593334280557910938},
journal = {Biometrika},
volume = {40},
pages = {237--264},
year = 1953
}

William A. Gale and Geoffrey Sampson (1995): Good-Turing frequency estimation without tears, Journal of Quantitative Linguistics

DELETEIntroduction   ResearchGroups   AvailableSoftware   SpeechTranslation   SignLanguage   ComputerAidedTranslation   OtherApproaches   Deployment Data   ParallelCorpora   ComparableCorpora   Dictionaries   CorpusCleaning   SentenceAlignment   Truecasing   WordSegmentation   SpellingCorrection   SparseData   PivotLanguages   DomainAdaptation WordBasedModels   IBMModels   Symmetrization   WordAlignment     WordAlignmentBasedOnCoocurrence     WordAlignmentWithLinguisticAnnotation     WordAlignmentForSyntaxBasedModels     DiscriminativeWordAlignment     WordAlignmentCombination     WordAlignmentEvaluation     AlignmentOfSubsententialUnits   DictionariesFromComparableCorpora   LexicalChoice   MTWithoutWordAlignment PhraseBasedModels   PhraseBasedModelTraining   PhraseBasedModelEM   PhraseBasedModelRefinement   ContextFeatures   Smoothing   Paraphrasing   PruningLargeModels   SuffixArrays   ReorderingModels   PhraseBasedVsEBMT SyntaxBasedModels   InversionTransductionGrammars   HierarchicalModels   StringToTree   TreeToString   TreeToTree   BinarizingSCFG   GenerativeSyntaxModels   DependencyStructure   DeepSyntax   ParseQuality   ParallelTreebanks   SyntacticCoherence LinguisticProblems   UnknownWords   Morphology   GeneratingRichMorphology   FactoredTranslationModels   Compounds   TranslatingTenseCaseMarkers   TranslatingChunksAndClauses   DocumentLevelAspects   Transliteration     TransliterationWithFSM     TransliterationWithOtherMethods     ForwardTransliteration     TransliterationTrainingData     IntegratingTransliteration   SyntacticPrereordering   POSChunkPrereordering   SyntacticReranking   IntegratingSyntacticFeatures LanguageModels   NGramLanguageModels   TargetedLanguageModels   MorphologicalLanguageModels   VeryLargeLanguageModels Decoding   StackDecoding   ReorderingConstraints   WordBasedDecoding   FiniteStateMachines   DecodingComplexity   SyntaxModelDecoding Evaluation   EvaluationCampaigns   ManualMetrics   TaskBasedMetrics   EditRateMetrics   NGramMetrics   SyntaxBasedMetrics   CorrelationOfMetrics   TrainedMetrics   TranslationDifficulty   StatisticalSignificance MachineLearning   WordGraphs   ParameterTuning   Reranking   LargeScaleDiscriminativeTraining   MinimumBayesRisk   ConfidenceMeasures   SystemCombination   IncrementalUpdating NeuralNetworkModels   NeuralLanguageModels   AttentionModel   Training   Inference   Coverage   Vocabulary   Embeddings   MultilingualWordEmbeddings   MonolingualData   Adaptation   LinguisticAnnotation   MultilingualMultimodalMultitask   AlternativeArchitectures   AnalysisAndVisualization   NeuralComponentsInStatisticalMachineTranslation add

@Article{GoodTuringIntro,
author = {William A. Gale and Geoffrey Sampson},
title = {Good-{T}uring frequency estimation without tears},
url = {http://faculty.cs.byu.edu/~ringger/CS479/papers/Gale-SimpleGoodTuring.pdf},
googlescholar = {17913154986383214074},
journal = {Journal of Quantitative Linguistics},
volume = {2},
year = 1995
}

Ian H. Witten and Timothy C. Bell (1991): The zero-frequency problem: estimating the probabilities of novelevents in adaptive text compression, IEEE Transactions on Information Theory

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@article{witten-bell,
author = {Ian H. Witten and Timothy C. Bell},
title = {The zero-frequency problem: estimating the probabilities of novelevents in adaptive text compression},
url = {http://vuz.zaznai.ru/tw\_files2/urls\_5/27/d-26334/7z-docs/2.pdf},
googlescholar = {13874685675690416934},
journal = {IEEE Transactions on Information Theory},
volume = {37},
number = {4},
pages = {1085--1094},
year = 1991
}

Reinhard Kneser and Hermann Ney (1995): Improved Backing-Off for M-Gram Language Modeling, Proceedings of the IEEE International Conference on Accoustics, Speech and Signal Processing

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@InProceedings{kneser-ney,
author = {Reinhard Kneser and Hermann Ney},
title = {Improved Backing-Off for M-Gram Language Modeling},
booktitle = {Proceedings of the IEEE International Conference on Accoustics, Speech and Signal Processing},
volume = {1},
year = 1995
}

Stanley F. Chen and Joshua Goodman (1998): An Emprirical Study of Smoothing Techniques for Language Modeling

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@Techreport{ChenGoodman,
author = {Stanley F. Chen and Joshua Goodman},
title = {An Emprirical Study of Smoothing Techniques for Language Modeling},
url = {http://acl.ldc.upenn.edu/P/P96/P96-1041.pdf},
googlescholar = {3282313242724057405},
month = {August},
institution = {Computer Science Group, Harvard University},
number = {TR-10-98},
year = 1998
}

Radu Soricut and Kevin Knight and Daniel Marcu (2002): Using a large monolingual corpus to improve translation accuracy, Machine Translation: From Research to Real Users, 5th Conference of the Association for Machine Translation in the Americas, AMTA 2002 Tiburon, CA, USA, October 6-12, 2002, Proceedings

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@inproceedings{Soricut:2002,
author = {Radu Soricut and Kevin Knight and Daniel Marcu},
title = {Using a large monolingual corpus to improve translation accuracy},
editor = {Stephen D. Richardson},
booktitle = {Machine Translation: From Research to Real Users, 5th Conference of the Association for Machine Translation in the Americas, AMTA 2002 Tiburon, CA, USA, October 6-12, 2002, Proceedings},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = {2499},
isbn = {3-540-44282-0},
bibsource = {DBLP, http://dblp.uni-trier.de},
year = 2002
}

Benchmarks

Discussion

Related Topics

New Publications

• Markus Freitag and Minwei Feng and Matthias Huck and Stephan Peitz and Hermann Ney (2013): Reverse Word Order Model, Machine Translation Summit XIV

  DELETEIntroduction   ResearchGroups   AvailableSoftware   SpeechTranslation   SignLanguage   ComputerAidedTranslation   OtherApproaches   Deployment Data   ParallelCorpora   ComparableCorpora   Dictionaries   CorpusCleaning   SentenceAlignment   Truecasing   WordSegmentation   SpellingCorrection   SparseData   PivotLanguages   DomainAdaptation WordBasedModels   IBMModels   Symmetrization   WordAlignment     WordAlignmentBasedOnCoocurrence     WordAlignmentWithLinguisticAnnotation     WordAlignmentForSyntaxBasedModels     DiscriminativeWordAlignment     WordAlignmentCombination     WordAlignmentEvaluation     AlignmentOfSubsententialUnits   DictionariesFromComparableCorpora   LexicalChoice   MTWithoutWordAlignment PhraseBasedModels   PhraseBasedModelTraining   PhraseBasedModelEM   PhraseBasedModelRefinement   ContextFeatures   Smoothing   Paraphrasing   PruningLargeModels   SuffixArrays   ReorderingModels   PhraseBasedVsEBMT SyntaxBasedModels   InversionTransductionGrammars   HierarchicalModels   StringToTree   TreeToString   TreeToTree   BinarizingSCFG   GenerativeSyntaxModels   DependencyStructure   DeepSyntax   ParseQuality   ParallelTreebanks   SyntacticCoherence LinguisticProblems   UnknownWords   Morphology   GeneratingRichMorphology   FactoredTranslationModels   Compounds   TranslatingTenseCaseMarkers   TranslatingChunksAndClauses   DocumentLevelAspects   Transliteration     TransliterationWithFSM     TransliterationWithOtherMethods     ForwardTransliteration     TransliterationTrainingData     IntegratingTransliteration   SyntacticPrereordering   POSChunkPrereordering   SyntacticReranking   IntegratingSyntacticFeatures LanguageModels   NGramLanguageModels   TargetedLanguageModels   MorphologicalLanguageModels   VeryLargeLanguageModels Decoding   StackDecoding   ReorderingConstraints   WordBasedDecoding   FiniteStateMachines   DecodingComplexity   SyntaxModelDecoding Evaluation   EvaluationCampaigns   ManualMetrics   TaskBasedMetrics   EditRateMetrics   NGramMetrics   SyntaxBasedMetrics   CorrelationOfMetrics   TrainedMetrics   TranslationDifficulty   StatisticalSignificance MachineLearning   WordGraphs   ParameterTuning   Reranking   LargeScaleDiscriminativeTraining   MinimumBayesRisk   ConfidenceMeasures   SystemCombination   IncrementalUpdating NeuralNetworkModels   NeuralLanguageModels   AttentionModel   Training   Inference   Coverage   Vocabulary   Embeddings   MultilingualWordEmbeddings   MonolingualData   Adaptation   LinguisticAnnotation   MultilingualMultimodalMultitask   AlternativeArchitectures   AnalysisAndVisualization   NeuralComponentsInStatisticalMachineTranslation add

  

  @inproceedings{MTS2013-Freitag,
  author = {Markus Freitag and Minwei Feng and Matthias Huck and Stephan Peitz and Hermann Ney},
  title = {Reverse Word Order Model},
  url = {http://www.mt-archive.info/10/MTS-2013-Freitag.pdf},
  pages = {159--166},
  booktitle = {Machine Translation Summit XIV},
  year = 2013
  }
  Freitag et al. (2013)