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CUNI Experiments for WMT17 Metrics Task

机译:WUNI17指标任务的CUNI实验

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摘要

In this paper, we propose three different methods for automatic evaluation of the machine translation (MT) quality Two of the metrics are trainable on direct-assessment scores and two of them use dependency structures. The trainable metric AutoDA, which uses deep-syntactic features, achieved better correlation with humans compared e.g. to the chrF3 metric.
机译:在本文中,我们提出了三种不同的方法来自动评估机器翻译(MT)的质量,其中两个指标可在直接评估分数上进行训练,而其中两个则使用依赖项结构。使用深度句法功能的可训练度量标准AutoDA与例如到chrF3指标。

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  • 来源
  • 会议地点 Copenhagen(DK)
  • 作者单位

    Charles University, Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics Malostranske namesti 25, 118 00 Prague, Czech Republic;

    Charles University, Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics Malostranske namesti 25, 118 00 Prague, Czech Republic;

    Charles University, Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics Malostranske namesti 25, 118 00 Prague, Czech Republic;

    Charles University, Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics Malostranske namesti 25, 118 00 Prague, Czech Republic;

    Charles University, Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics Malostranske namesti 25, 118 00 Prague, Czech Republic;

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  • 正文语种 eng
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