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.

神经机器翻译在近年来取得了快速的进展，我们提出了一种双向语义评估方法，通过计算源文本与翻译的语义距离，实现了在同一语言层面上的句子比较，该方法在英德语对的多个机器翻译系统中得出的平均评估分数与人工评估之间存在强相关性，同时提出了一种新的多语言方法来对 MT 系统进行排序，无需平行语料库。