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    <identifier>10.11922/sciencedb.j00104.00028</identifier>
    <datestamp>2020-10-16T13:34:17Z</datestamp>
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  <dc:date>2020-10-16</dc:date>
  <dc:title>MDKB-Bot: A Practical Framework for Multi-Domain Task-Oriented Dialogue System</dc:title>
  <dc:identifier>doi:10.11922/sciencedb.j00104.00028</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description> Four tables and two figures of this paper. Table 1 shows the statistics of training corpus for domain classification. Table 2 summarizes the statistics of training corpus for slot filling. Table 3 is  a comparison of labeling performance on NLU. Table 4 shows dialogue quality evaluation results between top 4 teams in the competition. Figure 1 depicts the overall framework of the model consists of three components: (1) NLU module, which predicts intent domain and gives slot value of user utterance, (2) DM module, which outputs the dialogue action to NLG module, and (3) NLG module, which generates the final response. Figure 2 presents BLSTM-CRF model for slot filling. </dc:description>
  <dc:subject>Dialogue system;  Knowledge base;  Natural language understanding;  Slot filling;  Natural language generation</dc:subject>
  <dc:creator>Yadi Lao</dc:creator>
  <dc:creator>Weijie Liu</dc:creator>
  <dc:creator>Sheng Gao</dc:creator>
  <dc:creator> Si Li</dc:creator>
  <dc:rights>PUBLIC</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:relation>http://www.doi.org/10.1162/dint_a_00010</dc:relation>
  <dc:publisher>Science Data Bank</dc:publisher>
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