Conference paper Open Access

Learning How to Simplify From Explicit Labeling of Complex-Simplified Text Pairs

Fernando Alva-Manchego; Joachim Bingel; Gustavo Henrique Paetzold; Carolina Scarton; Lucia Specia


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  <identifier identifierType="DOI">10.5281/zenodo.1042505</identifier>
  <creators>
    <creator>
      <creatorName>Fernando Alva-Manchego</creatorName>
      <affiliation>University of Sheffield</affiliation>
    </creator>
    <creator>
      <creatorName>Joachim Bingel</creatorName>
      <affiliation>University of Copenhagen</affiliation>
    </creator>
    <creator>
      <creatorName>Gustavo Henrique Paetzold</creatorName>
      <affiliation>University of Sheffield</affiliation>
    </creator>
    <creator>
      <creatorName>Carolina Scarton</creatorName>
      <affiliation>University</affiliation>
    </creator>
    <creator>
      <creatorName>Lucia Specia</creatorName>
      <affiliation>Univer</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Learning How to Simplify From Explicit Labeling of Complex-Simplified Text Pairs</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2017</publicationYear>
  <dates>
    <date dateType="Issued">2017-11-27</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="ConferencePaper"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/1042505</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.1042504</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/h2020-simpatico-692819</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;Current research in text simplification has been hampered by two central problems: (i) the small amount of high-quality parallel simplification data available, and (ii) the lack of explicit annotations of simplification operations, such as deletions or substitutions, on existing data. While the recently introduced Newsela corpus&amp;nbsp;has alleviated the first problem, simplifications still need to be learned directly from parallel text using black-box, end-to-end approaches rather than from explicit annotations. These complex-simple parallel sentence pairs often differ to such a high degree that generalization becomes difficult. &amp;nbsp;End-to-end models also make it hard to interpret what is actually learned from data. &amp;nbsp;We propose a method that decomposes the task of TS into its sub-problems. We devise a way to automatically identify operations in a parallel corpus and introduce a sequence-labeling approach based on these annotations. Finally, we provide insights on the types of transformations that different approaches can model.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/692819/">692819</awardNumber>
      <awardTitle>SIMplifying the interaction with Public Administration Through Information technology for Citizens and cOmpanies</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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