Conference paper Open Access

A Case Study of Closed-Domain Response Suggestion with Limited Training Data

Galke, Lukas; Gerstenkorn, Gunnar; Scherp, Ansgar


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  <identifier identifierType="URL">https://zenodo.org/record/2583130</identifier>
  <creators>
    <creator>
      <creatorName>Galke, Lukas</creatorName>
      <givenName>Lukas</givenName>
      <familyName>Galke</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-6124-1092</nameIdentifier>
      <affiliation>Kiel University</affiliation>
    </creator>
    <creator>
      <creatorName>Gerstenkorn, Gunnar</creatorName>
      <givenName>Gunnar</givenName>
      <familyName>Gerstenkorn</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-4889-511X</nameIdentifier>
      <affiliation>University of Potsdam</affiliation>
    </creator>
    <creator>
      <creatorName>Scherp, Ansgar</creatorName>
      <givenName>Ansgar</givenName>
      <familyName>Scherp</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-2653-9245</nameIdentifier>
      <affiliation>University of Stirling</affiliation>
    </creator>
  </creators>
  <titles>
    <title>A Case Study of Closed-Domain Response Suggestion with Limited Training Data</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2018</publicationYear>
  <subjects>
    <subject>conversational agents</subject>
    <subject>neural networks</subject>
    <subject>representation learning</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2018-09-06</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/2583130</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1007/978-3-319-99133-7_18</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/moving-h2020</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://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;We analyze the problem of response suggestion in a closed domain along a real-world scenario of a digital library. We present a text-processing pipeline to generate question-answer pairs from chat transcripts. On this limited amount of training data, we compare retrieval-based, conditioned-generation, and dedicated representation learning approaches for response suggestion. Our results show that retrieval-based methods that strive to find similar, known contexts are preferable over parametric approaches from the conditioned-generation family, when the training data is limited. We, however, identify a specific representation learning approach that is competitive to the retrieval-based approaches despite the training data limitation.&lt;/p&gt;</description>
    <description descriptionType="Other">This is a post-peer-review, pre-copyedit version of a paper published in Elloumi M, Granitzer M, Hameurlain A, Seifert C, Stein
B, Tjoa A &amp; Wagner R (eds.) Database and Expert Systems Applications. DEXA 2018. Communications in Computer and
Information Science, 903. The final authenticated version is available online at: https://doi.org/10.1007/978-3-319-99133-7_18</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/693092/">693092</awardNumber>
      <awardTitle>Training towards a society of data-savvy information professionals to enable open leadership innovation</awardTitle>
    </fundingReference>
  </fundingReferences>
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