Translation ranking is inherently of great significance for machine translation (MT), as it allows the comparison of performances of multiple MT systems as well as for its efficient training. This article demonstrates a mechanism that is used for ranking the translation outputs generated by the MT systems from best to worst. To implement this approach, the system exploits a supervised learning algorithm trained over existing manual ranking by using various features obtained after the linguistic analysis of both source and target side sentences without relying on the reference translation.

中文翻译：

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基于质量的翻译输出排名

翻译排名对于机器翻译 (MT) 具有内在的重要意义，因为它允许比较多个 MT 系统的性能及其有效训练。本文演示了一种机制，用于将 MT 系统生成的翻译输出从最佳到最差进行排序。为了实现这种方法，该系统通过使用在源和目标副句的语言分析后获得的各种特征，在不依赖参考翻译的情况下，利用在现有手动排名上训练的监督学习算法。