Language Model Adaptation for Statistical Machine Translation via Structured Query Models

We explore unsupervised language model adaptation techniques for Statistical Machine Translation. The hypotheses from the machine translation output are converted into queries at different levels of representation power and used to extract similar sentences from very large monolingual text collection. Specific language models are then build from the retrieved data and interpolated with a general background model. Experiments show significant improvements when translating with these adapted language models.

• Publication year

  2004

• Venue

  International Conference on Computational Linguistics

• Publication date

  2004-08-23

• Fields of study

  Linguistics, Computer Science

• Identifiers
  DOI 10.3115/1220355.1220414
• 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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