Publication | Closed Access
Leveraging multiple languages to improve statistical MT word alignments
12
Citations
14
References
2005
Year
Unknown Venue
EngineeringHmm ModelsCross-lingual RepresentationMultilingual PretrainingCorpus LinguisticsText MiningSpeech RecognitionNatural Language ProcessingMultiple LanguagesLanguage DocumentationData ScienceComputational LinguisticsLanguage StudiesEuroparl CorpusMachine TranslationComputer-assisted TranslationLinguisticsSimple ExtensionNeural Machine TranslationSpeech Translation
We present a new multilingual statistical MT word alignment model based on a simple extension of the IBM and HMM models and a two-step alignment procedure. Preliminary results on a small hand-aligned subset of the Europarl corpus show a 7% relative improvement over a state of the art alignment model
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