Linking Translation Memories with Example-Based Machine Translation

@inproceedings{Carl1999LinkingTM,
  title={Linking Translation Memories with Example-Based Machine Translation},
  author={Michael Carl and Silvia Hansen},
  year={1999}
}
The paper reports on experiments which compare the translation outcome of three corpus-based MT systems, a string-based translation memory (STM), a lexeme-based translation memory (LTM) and the example- based machine translation (EBMT) sys- tem EDGAR. We use a fully automatic evaluation method to compare the outcome of each MT system and discuss the results. We investigate the benefits for the link- age of different MT strategies such as TM- systems and EBMT systems. is coded in the same way… CONTINUE READING

Figures, Results, and Topics from this paper.

Key Quantitative Results

  • 96.6% or 293 sentences of the EDGAR translations achieved a translation score of 66% or better while the lexeme and the STM translates 93.7% (284 sentences) and 95.0% (288 sentences) respectively with the same translation score.

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