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

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Digital Libraries at Times of Massive Societal Transition (ICADL 2020)

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

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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.

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Acknowledgements

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

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Authors and Affiliations

  1. College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan

    Yuting Song & Akira Maeda

  2. Kinugasa Research Organization, Ritsumeikan University, Kyoto, Japan

    Biligsaikhan Batjargal

Authors
  1. Yuting Song
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  2. Biligsaikhan Batjargal
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  3. Akira Maeda
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Corresponding author

Correspondence to Yuting Song .

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Editors and Affiliations

  1. Kyushu University, Fukuoka, Japan

    Emi Ishita

  2. National University of Singapore, Singapore, Singapore

    Natalie Lee San Pang

  3. Wuhan University, Wuhan, China

    Lihong Zhou

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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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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64451-2

  • Online ISBN: 978-3-030-64452-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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