A Phrase-Based, Joint Probability for Statistical Machine Translation

Abstract

We present a joint probability model for statistical machine translation, which automatically learns word and phrase equivalents from bilingual corpora. Translations produced with parameters estimated using the joint model are more accurate than translations produced using IBM Model 4.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2002
Accession Number
ADA461277

Entities

People

  • Daniel Marcu
  • William Wong

Organizations

  • University of Southern California

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Applied Computer Science
  • Computational Linguistics
  • Computational Science
  • Decoding
  • Language
  • Linguistics
  • Machine Translation
  • Natural Language Processing
  • Natural Languages
  • Probability
  • Training
  • Translations

Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • Computational Modeling and Simulation

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Translation