Improving a general-purpose Statistical Translation Engine by Terminological lexicons

The past decade has witnessed exciting work in the field of Statistical Machine Translation (SMT). However, accurate evaluation of its potential in real-life contexts is still a questionable issue.In this study, we investigate the behavior of an SMT engine faced with a corpus far different from the one it has been trained on. We show that terminological databases are obvious resources that should be used to boost the performance of a statistical engine. We propose and evaluate a way of integrating terminology into a SMT engine which yields a significant reduction in word error rate.

• Publication year

  2002

• Venue

  International Conference on Computational Linguistics

• Publication date

  2002-08-31

• Fields of study

  Linguistics, Computer Science

• Identifiers
  DOI 10.3115/1118771.1118776
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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