November 2, 2019 Conference paper Open Access

Philipp Wiesenbach; Stefan Riezler

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  <identifier identifierType="DOI">10.5281/zenodo.3524924</identifier>
  <creators>
    <creator>
      <creatorName>Philipp Wiesenbach</creatorName>
      <affiliation>Computational Linguistics, Heidelberg University, Germany</affiliation>
    </creator>
    <creator>
      <creatorName>Stefan Riezler</creatorName>
      <affiliation>Computational Linguistics &amp; IWR, Heidelberg University, Germany</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Multi-Task Modeling of Phonographic Languages: Translating Middle Egyptian Hieroglyphs</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <dates>
    <date dateType="Issued">2019-11-02</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3524924</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3524923</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/iwslt2019</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Machine translation of ancient languages faces a low-resource problem, caused by the limited amount of available textual source data&amp;nbsp;and their translations. We present a multi-task modeling approach to translating Middle Egyptian that is inspired by recent successful&amp;nbsp;approaches to multi-task learning in end-to-end speech translation. We leverage the phonographic aspect of the hieroglyphic writing&amp;nbsp;system, and show that similar to multi-task learning of speech recognition and translation, joint learning and sharing of structural&amp;nbsp;information between hieroglyph transcriptions, translations, and POS tagging can improve direct translation of hieroglyphs by several&amp;nbsp;BLEU points, using a minimal amount of manual transcriptions.&lt;/p&gt;</description>
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