Sparse Data

Building machine translation systems for under-resourced languages or in the face of sparse data conditions for other reasons, is a special challenge, and may require special methods.

Sparse Data is the main subject of 15 publications. 11 are discussed here.

Publications

Several reports show how statistical machine translation allows for rapid development with limited resources (Al-Onaizan et al., 2000; Al-Onaizan et al., 2002; Foster et al., 2003; Oard and Och, 2003). A practical example of this is the rapid development of a Haitian Creole to English machine translation systems for first responder assistance for the aftermath of the 2010 earthquake in the country (Lewis et al., 2011). The training data made available and extended during this effort was the topic of a shared task (Callison-Burch et al., 2011), where several research teams participated (Eidelman et al., 2011; Hewavitharana et al., 2011; Hu et al., 2011; Stymne, 2011). Another good example study is the development of a Yiddish-English system Genzel et al. (2009), where a range of methods were explored, such as taking advantages of the close relation of Yiddish to German and the existence of Polish and Hebrew loan words.

Benchmarks

A shared task on Haitian Creole organized at the 2011 ACL Workshop on statistical machine translation (Callison-Burch et al., 2011) provides a data set that has been used by several research groups.

Discussion

Related Topics

Sparse data increases the problem of Unknown Words, which may be replaced by Paraphrasing. If training data into a bridge language is available, such Pivot Languages can be exploited. The need to make use of any available data resources, even Comparable Corpora, is more urgent.

In general, since many methods in statistical machine translations are geared towards making effective use of the training data, they will be more likely make a difference in a sparse data scenario.

New Publications

• Jeff Ma and Spyros Matsoukas and Richard Schwartz (2011): Improving Low-Resource Statistical Machine Translation with a Novel Semantic Word Clustering Algorithm, Proceedings of the 13th Machine Translation Summit (MT Summit XIII)

  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{MTS-2011-Ma-2,
  author = {Jeff Ma and Spyros Matsoukas and Richard Schwartz},
  title = {Improving Low-Resource Statistical Machine Translation with a Novel Semantic Word Clustering Algorithm},
  url = {http://www.mt-archive.info/MTS-2011-Ma-2.pdf},
  pages = {352-359},
  booktitle = {Proceedings of the 13th Machine Translation Summit (MT Summit XIII)},
  publisher = {International Association for Machine Translation},
  location = {Xiamen, China},
  year = 2011
  }
  Ma et al. (2011)
• Steve DeNeefe and Ulf Hermjakob and Kevin Knight (2008): Overcoming Vocabulary Sparsity in MT Using Lattices, Proceedings of the Eighth Conference of the Association for Machine Translation in the Americas (AMTA)

  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{amta08:DeNeefe,
  author = {Steve DeNeefe and Ulf Hermjakob and Kevin Knight},
  title = {Overcoming Vocabulary Sparsity in {MT} Using Lattices},
  url = {http://www.isi.edu/natural-language/mt/amta2008su.pdf},
  googlescholar = {4025401116724932831},
  pages = {89--96},
  booktitle = {Proceedings of the Eighth Conference of the Association for Machine Translation in the Americas (AMTA)},
  location = {Waikiki, Hawaii},
  year = 2008
  }
  DeNeefe et al. (2008)
• Wang, Pidong and Nakov, Preslav and Ng, Hwee Tou (2012): Source Language Adaptation for Resource-Poor Machine Translation, Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

  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{wang-nakov-ng:2012:EMNLP-CoNLL,
  author = {Wang, Pidong and Nakov, Preslav and Ng, Hwee Tou},
  title = {Source Language Adaptation for Resource-Poor Machine Translation},
  booktitle = {Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning},
  month = {July},
  address = {Jeju Island, Korea},
  publisher = {Association for Computational Linguistics},
  pages = {286--296},
  url = {http://www.aclweb.org/anthology/D12-1027},
  year = 2012
  }
  Wang et al. (2012)
• William Lewis and Phong Yang (2012): Building MT for a Severely Under-Resourced Language: White Hmong, Proceedings of the Tenth Conference of the Association for Machine Translation in the Americas (AMTA)

  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{AMTA-2012-Lewis,
  author = {William Lewis and Phong Yang },
  title = {Building {MT} for a Severely Under-Resourced Language: White Hmong},
  url = {http://www.mt-archive.info/AMTA-2012-Lewis.pdf},
  booktitle = {Proceedings of the Tenth Conference of the Association for Machine Translation in the Americas (AMTA)},
  location = {San Diego, California},
  year = 2012
  }
  Lewis and Yang (2012)