Improving Domain-specific Machine Translation by Constraining the Language Model

Abstract

A domain-specific statistical machine translation engine is shown to be more accurate when only domain-specific language data are used to build the target-language language model. This has been found to be true when compared to using a much larger, out-of-domain corpus for building the language model, either alone or in combination with the domain-specific data.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2012
Accession Number
ADA568649

Entities

People

  • Jeffrey C. Micher

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Algorithms
  • Applied Computer Science
  • Army
  • Computational Linguistics
  • Computational Science
  • Information Retrieval
  • Information Science
  • Language
  • Language Translation
  • Linguistics
  • Machine Translation
  • Military Training
  • Natural Language Processing
  • Natural Languages
  • Probability
  • Training
  • Translations

Fields of Study

  • Computer science

Readers

  • Computational Linguistics

Technology Areas

  • AI & ML
  • AI & ML - Machine Translation