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International Conference on Asian Digital Libraries

ICADL 2020: Digital Libraries at Times of Massive Societal Transition pp 262–268Cite as

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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  • DOI: 10.1007/978-3-030-64452-9_24
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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

    Assoc. Prof. Emi Ishita

  2. National University of Singapore, Singapore, Singapore

    Natalie Lee San Pang

  3. Wuhan University, Wuhan, China

    Prof. 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

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