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文章基本信息

  • 标题:Fine-Grained Human Evaluation of Neural Versus Phrase-Based Machine Translation
  • 本地全文:下载
  • 作者:Filip Klubička ; Antonio Toral ; Víctor M. Sánchez-Cartagena
  • 期刊名称:The Prague Bulletin of Mathematical Linguistics
  • 印刷版ISSN:0032-6585
  • 电子版ISSN:1804-0462
  • 出版年度:2017
  • 卷号:108
  • 期号:1
  • 页码:121-132
  • DOI:10.1515/pralin-2017-0014
  • 语种:English
  • 出版社:Walter de Gruyter GmbH
  • 摘要:We compare three approaches to statistical machine translation (pure phrase-based, factored phrase-based and neural) by performing a fine-grained manual evaluation via error annotation of the systems’ outputs. The error types in our annotation are compliant with the multidimensional quality metrics (MQM), and the annotation is performed by two annotators. Inter-annotator agreement is high for such a task, and results show that the best performing system (neural) reduces the errors produced by the worst system (phrase-based) by 54%.
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