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Referential Translation Machines for Predicting Translation Quality and Related Statistics

机译:参考翻译机预测翻译质量和相关统计数据

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摘要

We use referential translation machines (RTMs) for predicting translation performance. RTMs pioneer a language independent approach to all similarity tasks and remove the need to access any task or domain specific information or resource. We improve our RTM models with the ParFDA instance selection model (Bicici et al., 2015), with additional features for predicting the translation performance, and with improved learning models. We develop RTM models for each WMT15 QET (QET15) subtask and obtain improvements over QET14 results. RTMs achieve top performance in QET15 ranking 1st in document- and sentence-level prediction tasks and 2nd in word-level prediction task.
机译:我们使用参考翻译机(RTM)来预测翻译效果。 RTM开创了一种语言无关的方法来处理所有相似性任务,并且无需访问任何任务或特定于域的信息或资源。我们使用ParFDA实例选择模型(Bicici等人,2015),具有预测翻译性能的其他功能以及改进的学习模型来改进RTM模型。我们为每个WMT15 QET(QET15)子任务开发RTM模型,并获得对QET14结果的改进。 RTM在QET15中获得最高的性能,在文档和句子级别的预测任务中排名第一,在单词级别的预测任务中排名第二。

著录项

  • 来源
  • 会议地点 Lisbon(PT)
  • 作者

    Ergun Bicici; Qun Liu; Andy Way;

  • 作者单位

    ADAPT Research Center School of Computing Dublin City University, Ireland;

    ADAPT Research Center School of Computing Dublin City University, Ireland;

    ADAPT Research Center School of Computing Dublin City University, Ireland;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
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