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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.
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Contributor : Octavia Efraim <>
Submitted on : Saturday, June 2, 2018 - 8:44:08 PM
Last modification on : Monday, January 20, 2020 - 3:18:29 PM
Document(s) archivé(s) le : Monday, September 3, 2018 - 3:33:12 PM


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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⟩



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