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BoTest: a Framework to Test the Quality of Conversational Agents Using Divergent Input Examples
Ruane, Elayne; Faure, Théo; Smith, Ross; Bean, Dan; Carson-Berndsen, Julie; Ventresque, Anthony
ACM IUI (Intelligent User Interfaces), Tokyo, Japan, 07-11 March 2018 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. Science Foundation Ireland Lero
Keyword(s): Conversational agent testing; Conversational agent quality assessment; Chatbot
Publication Date:
2018
Type: Other
Peer-Reviewed: Unknown
Language(s): English
Institution: University College Dublin
Publisher(s): ACM
First Indexed: 2019-05-11 06:33:00 Last Updated: 2019-05-11 06:33:00