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首页> 外文会议>8th Workshop on syntax, semantics and structure in statistical translation 2014 >Better Semantic Frame Based MT Evaluation via Inversion Transduction Grammars
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Better Semantic Frame Based MT Evaluation via Inversion Transduction Grammars

机译：通过逆转转语法更好地基于语义框架的MT评估

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摘要

We introduce an inversion transduction grammar based restructuring of the MEANT automatic semantic frame based MT evaluation metric, which, by leveraging ITG language biases, is able to further improve upon MEANT's already-high correlation with human adequacy judgments. The new metric, called IMEANT, uses bracketing ITGs to biparse the reference and machine translations, but subject to obeying the semantic frames in both. Resulting improvements support the presumption that ITGs, which constrain the allowable permutations between compositional segments across the reference and MT output, score the phrasal similarity of the semantic role fillers more accurately than the simple word alignment heuristics (bag-of-word alignment or maximum alignment) used in previous version of MEANT. The approach successfully integrates (1) the previously demonstrated extremely high coverage of cross-lingual semantic frame alternations by ITGs, with (2) the high accuracy of evaluating MT via weighted f-scores on the degree of semantic frame preservation.
机译：我们介绍了一种基于逆转导语法的基于MEANT自动语义框架的MT评估指标的重构方法，该方法通过利用ITG语言偏见，能够进一步提高MEANT与人类充分性判断的高度相关性。称为IMEANT的新度量标准使用括号括起来的ITG来双重解析参考和机器翻译，但要服从两者的语义框架。最终的改进支持这样的假设，即ITG限制了参考和MT输出之间组成部分之间允许的排列，比简单的单词对齐启发式方法（单词袋对齐或最大对齐）更准确地对语义角色填充词的短语相似性进行评分）在以前的MEANT版本中使用。该方法成功地集成了（1）先前证明的ITG跨语言语义框架替换的极高覆盖率，以及（2）通过加权f分数评估语义框架保存程度的MT的高精度。

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  • 会议地点 Doha(QA)
  • 作者

    Dekai Wu; Lo Chi-kiu; Meriem Beloucif; Markus Saers;

  • 作者单位

    HKUST Human Language Technology Center Department of Computer Science and Engineering Hong Kong University of Science and Technology;

    HKUST Human Language Technology Center Department of Computer Science and Engineering Hong Kong University of Science and Technology;

    HKUST Human Language Technology Center Department of Computer Science and Engineering Hong Kong University of Science and Technology;

    HKUST Human Language Technology Center Department of Computer Science and Engineering Hong Kong University of Science and Technology;

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