A method of generating translations of unseen n-grams by using proportional analogy

Juan Luo*, Yves Lepage

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


In recent years, statistical machine translation has gained much attention. The phrase-based statistical machine translation model has made significant advancement in translation quality over the word-based model. In this paper, we attempt to apply the technique of proportional analogy to statistical machine translation systems. We propose a novel approach to apply proportional analogy to generate translations of unseen n-grams from the phrase table for phrase-based statistical machine translation. Experiments are conducted with two datasets of different sizes. We also investigate two methods to integrate n-grams translations produced by proportional analogy into the state-of-the-art statistical machine translation system, Moses.1 The experimental results show that unseen n-grams translations generated using the technique of proportional analogy are rewarding for statistical machine translation systems with small datasets.

Original languageEnglish
Pages (from-to)325-330
Number of pages6
JournalIEEJ Transactions on Electrical and Electronic Engineering
Issue number3
Publication statusPublished - 2016 May 1


  • Phrase table
  • Proportional analogy
  • Statistical machine translation
  • Unseen n-grams

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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