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2020 | OriginalPaper | Chapter

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

Authors : Yuting Song, Biligsaikhan Batjargal, Akira Maeda

Published in: Digital Libraries at Times of Massive Societal Transition

Publisher: Springer International Publishing

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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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Metadata
Title
A Preliminary Attempt to Evaluate Machine Translations of Ukiyo-e Metadata Records
Authors
Yuting Song
Biligsaikhan Batjargal
Akira Maeda
Copyright Year
2020
DOI
https://doi.org/10.1007/978-3-030-64452-9_24

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