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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        <foaf:givenName>Gunnar</foaf:givenName>
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        <foaf:name>Scherp, Ansgar</foaf:name>
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    <dct:title>A Case Study of Closed-Domain Response Suggestion with Limited Training Data</dct:title>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2018</dct:issued>
    <dcat:keyword>conversational agents</dcat:keyword>
    <dcat:keyword>neural networks</dcat:keyword>
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        <foaf:name>European Commission</foaf:name>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2018-09-06</dct:issued>
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    <dct:description>&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;</dct:description>
    <dct:description xml:lang="">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</dct:description>
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