Neural machine translation (NMT) has progressed rapidly in the past few years, promising improvements and quality translations for different languages. Evaluation of this task is crucial to determine the quality of the translation. Overall, insufficient emphasis is placed on the actual sense of the translation in traditional methods. We propose a bidirectional semantic-based evaluation method designed to assess the sense distance of the translation from the source text. This approach employs the comprehensive multilingual encyclopedic dictionary BabelNet. Through the calculation of the semantic distance between the source and its back translation of the output, our method introduces a quantifiable approach that empowers sentence comparison on the same linguistic level. Factual analysis shows a strong correlation between the average evaluation scores generated by our method and the human assessments across various machine translation systems for English-German language pair. Finally, our method proposes a new multilingual approach to rank MT systems without the need for parallel corpora.

翻译：论文摘要：神经机器翻译（NMT）近年来发展迅速，为不同语言带来了翻译质量的提升。然而，翻译评估任务对确定译文质量至关重要。传统方法普遍对译文的实际语义关注不足。本文提出一种基于双向语义的评估方法，旨在衡量译文与源文本之间的语义距离。该方法采用多语言百科全书式词典BabelNet，通过计算源文本与其反向译文之间的语义距离，引入了一种可量化的方法，实现在同一语言层面进行句子比较。事实分析表明，在英德语言对的多种机器翻译系统中，本方法生成的平均评估分数与人工评分之间存在强相关性。最后，本方法提出了一种无需平行语料库即可对机器翻译系统进行排序的多语言新方法。