Adapting Machine Translation Engines to the Needs of Cultural Heritage Metadata.

The Europeana digital library features cultural heritage collections from over 3,000 European institutions described in 37 languages. However, most textual metadata describe the records in a single language, the data providers' language. Improving Europeana's multilingual accessibility presents chal...

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Publicado en:Information Technology & Libraries Vol. 43; no. 3; pp. 1 - 18
Autores principales: Chatzitheodorou, Konstantinos, Kaldeli, Eirini, Isaac, Antoine, Scalia, Paolo, Grau Lacal, Carmen, Escrivá, MªÁngeles García
Formato: tables/charts Journal Article
Publicado: American Library Association Sep2024
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Sep2024
      vid: 43
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      pub: American Library Association
      place: Chicago, Illinois
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        atl: Adapting Machine Translation Engines to the Needs of Cultural Heritage Metadata.
      aug:
        au:
          Chatzitheodorou, Konstantinos
          Kaldeli, Eirini
          Isaac, Antoine
          Scalia, Paolo
          Grau Lacal, Carmen
          Escrivá, MªÁngeles García
        affil: Postdoctoral Researcher, Ionian University
      sug:
        subj:
          Culture Europe
          Multilingualism
          Translations
          Metadata
          Cultural Diversity
          English Language
          Natural Language Processing
          Information Retrieval
          Machine Learning
          Information Resources
          Access to Information
          Language
          Europe
          Vocabulary, Controlled
          Automation
          Electronic Publications
      ab: The Europeana digital library features cultural heritage collections from over 3,000 European institutions described in 37 languages. However, most textual metadata describe the records in a single language, the data providers' language. Improving Europeana's multilingual accessibility presents challenges due to the unique characteristics of cultural heritage metadata, often expressed in short phrases and using in-domain terminology. This work presents the EuropeanaTranslate project's approach and results, aimed at translating Europeana metadata records from 23 EU languages into English. Machine Translation engines were trained on a cleaned selection of bilingual and synthetic data from Europeana, including multilingual vocabularies and relevant cultural heritage repositories. Automatic translations were evaluated through standard metrics and human assessments by linguists and domain cultural heritage experts. The results showed significant improvements when compared to the generic engines used before the in-domain training as well as the eTranslation service for most languages. The EuropeanaTranslate engines have translated over 29 million metadata records on Europeana.eu. Additionally, the MT engines and training datasets are publicly available via the European Language Grid Catalogue and the ELRC-SHARE repository.
      pubtype: Academic Journal
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    language: English
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