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Boosting a Rule-Based Chatbot Using Statistics and User Satisfaction Ratings

  • Octavia Efraim
  • Vladislav Maraev
  • João Rodrigues
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 789)

Abstract

Using data from user-chatbot conversations where users have rated the answers as good or bad, we propose a more efficient alternative to a chatbot’s keyword-based answer retrieval heuristic. We test two neural network approaches to the near-duplicate question detection task as a first step towards a better answer retrieval method. A convolutional neural network architecture gives promising results on this difficult task.

Notes

Acknowledgements

This research is partly funded by the Regional Council of Brittany through an ARED grant. The present research was also partly supported by the CLARIN and ANI/3279/2016 grants. We are grateful to Telsi for providing the data.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Octavia Efraim
    • 1
  • Vladislav Maraev
    • 2
  • João Rodrigues
    • 3
  1. 1.LIDILE EA3874University of Rennes 2RennesFrance
  2. 2.CLASPUniversity of GothenburgGothenburgSweden
  3. 3.Department of Informatics, Faculty of SciencesUniversity of LisbonLisbonPortugal

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