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  5. BoTest: a Framework to Test the Quality of Conversational Agents Using Divergent Input Examples
 
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BoTest: a Framework to Test the Quality of Conversational Agents Using Divergent Input Examples

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Author(s)
Ruane, Elayne 
Faure, Théo 
Smith, Ross 
Bean, Dan 
Carson-Berndsen, Julie 
Ventresque, Anthony 
Uri
http://hdl.handle.net/10197/9304
Date Issued
11 March 2018
Date Available
09T09:37:35Z April 2018
Abstract
Quality of conversational agents is important as users have high expectations. Consequently, poor interactions may lead to the user abandoning the system. In this paper, we propose a framework to test the quality of conversational agents. Our solution transforms working input that the conversational agent accurately recognises to generate divergent input examples that introduce complexity and stress the agent. As the divergent inputs are based on known utterances for which we have the 'normal' outputs, we can assess how robust the conversational agent is to variations in the input. To demonstrate our framework we built ChitChatBot, a simple conversational agent capable of making casual conversation.
Sponsorship
Science Foundation Ireland
Other Sponsorship
Lero
Type of Material
Conference Publication
Publisher
ACM
Copyright (Published Version)
2018 ACM
Keywords
  • Conversational agent ...

  • Conversational agent ...

  • Chatbot

DOI
10.1145/3180308.3180373
Language
English
Status of Item
Peer reviewed
Part of
IUI'18 Proceedings of the 23rd International Conference on Intelligent User Interfaces Companion
Description
ACM IUI (Intelligent User Interfaces), Tokyo, Japan, 07-11 March 2018
ISBN
978-1-4503-5571-1/18/03
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Owning collection
Computer Science Research Collection
Scopus© citations
18
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Mar 21, 2023
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