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A Preliminary Attempt to Evaluate Machine Translations of Ukiyo-e Metadata Records

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 12504)

Abstract

Providing multilingual metadata records for digital objects is a way expanding access to digital cultural collections. Recent advancements in deep learning techniques have made machine translation (MT) more accurate. Therefore, we evaluate the performance of three well-known MT systems (i.e., Google Translate, Microsoft Translator, and DeepL Translator) in translating metadata records of ukiyo-e images from Japanese to English. We evaluate the quality of their translations with an automatic evaluation metric BLEU. The evaluation results show that DeepL Translator is better at translating ukiyo-e metadata records than Google Translate or Microsoft Translator, with Microsoft Translator performing the worst.

Keywords

  • Machine translation evaluation
  • Metadata translation
  • Ukiyo-e
  • Japanese-English

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Acknowledgements

This work was supported in part by JSPS KAKENHI Grant Number 20K12567, 20K20135, and 19KK0256.

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Correspondence to Yuting Song .

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Song, Y., Batjargal, B., Maeda, A. (2020). A Preliminary Attempt to Evaluate Machine Translations of Ukiyo-e Metadata Records. In: Ishita, E., Pang, N.L.S., Zhou, L. (eds) Digital Libraries at Times of Massive Societal Transition. ICADL 2020. Lecture Notes in Computer Science(), vol 12504. Springer, Cham. https://doi.org/10.1007/978-3-030-64452-9_24

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  • DOI: https://doi.org/10.1007/978-3-030-64452-9_24

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