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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
Links https://doi.org/10.1007/978-3-319-7...
Subject categories Computational linguistics

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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Denna text är utskriven från följande webbsida:
http://www.gu.se/english/research/publication/?publicationId=261210
Utskriftsdatum: 2019-08-26