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

Juan Luo, Yves Lepage

研究成果: Article査読

1 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)325-330
ページ数6
ジャーナルIEEJ Transactions on Electrical and Electronic Engineering
11
3
DOI
出版ステータスPublished - 2016 5 1

ASJC Scopus subject areas

  • 電子工学および電気工学

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