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Title
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Neural machine translation of artwork titles using Iconclass codes
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Author
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Abstract
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| We investigate the use of Iconclass in the context of neural machine translation for NL↔EN artwork titles. Iconclass is a widely used iconographic classification system used in the cultural heritage domain to describe and retrieve subjects represented in the visual arts. The resource contains keywords and definitions to encode the presence of objects, people, events and ideas depicted in artworks, such as paintings. We propose a simple concatenation approach that improves the quality of automatically generated title translations for artworks, by leveraging textual information extracted from Iconclass. Our results demonstrate that a neural machine translation system is able to exploit this metadata to boost the translation performance of artwork titles. This technology enables interesting applications of machine learning in resource-scarce domains in the cultural sector |
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Language
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English
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Source (book)
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Proceedings of LaTeCH-CLfL 2020, pp. 42–51, Barcelona, Spain (Online)
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Publication
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2020
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ISBN
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978-1-952148-34-7
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Volume/pages
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p. 42-51
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Full text (open access)
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