HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Book sections

Boosting a rule-based chatbot using statistics and user satisfaction ratings

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.
Document type :
Book sections
Complete list of metadata

Cited literature [32 references]  Display  Hide  Download

Contributor : Octavia Efraim Connect in order to contact the contributor
Submitted on : Saturday, June 2, 2018 - 8:44:08 PM
Last modification on : Monday, December 13, 2021 - 12:03:01 PM
Long-term archiving on: : Monday, September 3, 2018 - 3:33:12 PM


Files produced by the author(s)


  • HAL Id : hal-01806464, version 1


Octavia Efraim, Vladislav Maraev, João Rodrigues. Boosting a rule-based chatbot using statistics and user satisfaction ratings. Filchenkov A., Pivovarova L., Žižka J. (eds) Artificial Intelligence and Natural Language. AINL 2017. Communications in Computer and Information Science, vol 789. Springer, Cham, 2018. ⟨hal-01806464⟩



Record views


Files downloads