BiVert: Bidirectional Vocabulary Evaluation using Relations for Machine
  Translation
Carinne Cherf Yuval Pinter
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.