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Measuring Semantic Coherence of a Conversation

  • Svitlana Vakulenko
  • Maarten de Rijke
  • Michael Cochez
  • Vadim Savenkov
  • Axel PolleresEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11136)

Abstract

Conversational systems have become increasingly popular as a way for humans to interact with computers. To be able to provide intelligent responses, conversational systems must correctly model the structure and semantics of a conversation. We introduce the task of measuring semantic (in)coherence in a conversation with respect to background knowledge, which relies on the identification of semantic relations between concepts introduced during a conversation. We propose and evaluate graph-based and machine learning-based approaches for measuring semantic coherence using knowledge graphs, their vector space embeddings and word embedding models, as sources of background knowledge. We demonstrate how these approaches are able to uncover different coherence patterns in conversations on the Ubuntu Dialogue Corpus.

Notes

Acknowledgments

This work is supported by the project 855407 “Open Data for Local Communities” (CommuniData) of the Austrian Federal Ministry of Transport, Innovation and Technology (BMVIT) under the program “ICT of the Future.” Svitlana Vakulenko was supported by the EU H2020 programme under the MSCA-RISE agreement 645751 (RISE_BPM). Axel Polleres was supported under the Distinguished Visiting Austrian Chair Professors program hosted by The Europe Center of Stanford University. Maarten de Rijke was supported by Ahold Delhaize, Amsterdam Data Science, the Bloomberg Research Grant program, the China Scholarship Council, the Criteo Faculty Research Award program, Elsevier, the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement nr 312827 (VOX-Pol), the Google Faculty Research Awards program, the Microsoft Research Ph.D. program, the Netherlands Institute for Sound and Vision, the Netherlands Organisation for Scientific Research (NWO) under project nrs CI-14-25, 652.002.001, 612.001.551, 652.001.003, and Yandex. All content represents the opinion of the authors, which is not necessarily shared or endorsed by their respective employers and/or sponsors.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Svitlana Vakulenko
    • 1
  • Maarten de Rijke
    • 2
  • Michael Cochez
    • 3
    • 4
    • 5
  • Vadim Savenkov
    • 1
  • Axel Polleres
    • 1
    • 6
    • 7
    Email author
  1. 1.Vienna University of Economics and BusinessViennaAustria
  2. 2.University of AmsterdamAmsterdamThe Netherlands
  3. 3.Fraunhofer FITSankt AugustinGermany
  4. 4.Informatik 5, RWTH University AachenAachenGermany
  5. 5.Faculty of Information TechnologyUniversity of JyvaskylaJyvaskylaFinland
  6. 6.Complexity Science Hub ViennaViennaAustria
  7. 7.Stanford UniversityStanfordUSA

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