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Ruane, Elayne
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Ruane, Elayne
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Ruane, Elayne
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Now showing 1 - 2 of 2
- PublicationBoTest: a Framework to Test the Quality of Conversational Agents Using Divergent Input Examples(ACM, 2018-03-11)
; ; ; ; ; 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.770Scopus© Citations 21 - PublicationTraining a Chatbot with Microsoft LUIS: Effect of Intent Imbalance on Prediction AccuracyMicrosoft LUIS is a natural language understanding service used to train Chatbots. Imbalance in the utterance training set may cause the LUIS model to predict the wrong intent for a user's query. We discuss this problem and the training recommendations from Microsoft to improve prediction accuracy with LUIS. We perform batch testing on three training sets created from two existing datasets to explore the effectiveness of these recommendations.
498Scopus© Citations 3