Training a Chatbot with Microsoft LUIS: Effect of Intent Imbalance on Prediction Accuracy
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|Title:||Training a Chatbot with Microsoft LUIS: Effect of Intent Imbalance on Prediction Accuracy||Authors:||Ruane, Elayne; Young, Robert; Ventresque, Anthony||Permanent link:||http://hdl.handle.net/10197/11782||Date:||17-Mar-2020||Online since:||2020-12-04T12:42:55Z||Abstract:||Microsoft 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.||Funding Details:||Science Foundation Ireland||Funding Details:||Microsoft Corporation||Type of material:||Conference Publication||Publisher:||ACM||Copyright (published version):||2020 the Authors||Keywords:||Dataset imbalance; Chatbot; LUIS; Classification accuracy||DOI:||10.1145/3379336.3381494||Other versions:||http://iui.acm.org/2020/||Language:||en||Status of Item:||Peer reviewed||Is part of:||IUI '20: Proceedings of the 25th International Conference on Intelligent User Interfaces Companion||Conference Details:||The 25th International Conference on Intelligent User Interfaces Companion (IUI'20), Cagliari, Italy, 17-20 March 2020||ISBN:||9781450375139
|This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|
|Appears in Collections:||Computer Science Research Collection|
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