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文章基本信息

  • 标题:Language Models for Machine Translation: Original vs. Translated Texts
  • 本地全文:下载
  • 作者:Gennadi Lembersky ; Noam Ordan ; Shuly Wintner
  • 期刊名称:Computational Linguistics
  • 印刷版ISSN:0891-2017
  • 电子版ISSN:1530-9312
  • 出版年度:2012
  • 卷号:38
  • 期号:4
  • 页码:799-825
  • DOI:10.1162/COLI_a_00111
  • 语种:English
  • 出版社:MIT Press
  • 摘要:We investigate the differences between language models compiled from original target-language texts and those compiled from texts manually translated to the target language. Corroborating established observations of Translation Studies, we demonstrate that the latter are significantly better predictors of translated sentences than the former, and hence fit the reference set better. Furthermore, translated texts yield better language models for statistical machine translation than original texts.
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