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Boosting a Rule-Based Chatbot Using Statistics and User Satisfaction

Conference paper
Authors Octavia Efraim
Vladislav Maraev
João Rodrigues
Published in Artificial Intelligence and Natural Language: 6th Conference, AINL 2017, St. Petersburg, Russia, September 20--23, 2017. Revised Selected Papers / edited by Andrey Filchenkov, Lidia Pivovarova, Jan Žižka
ISBN 978-3-319-71745-6
Publisher Springer International Publishing
Place of publication Cham
Publication year 2017
Published at Department of Philosophy, Linguistics and Theory of Science
Language en
Subject categories Computational linguistics


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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