BiVert: Bidirectional Vocabulary Evaluation using Relations for Machine Translation
This provides a quantifiable, multilingual evaluation approach for machine translation systems without requiring parallel corpora, though it is incremental in improving evaluation methods.
The paper tackles the problem of evaluating machine translation quality by proposing a bidirectional semantic-based method that uses BabelNet to measure sense distance between source and back-translated output, showing strong correlation with human assessments for English-German translations.
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.