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, ElayneYoung, RobertVentresque, 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 imbalanceChatbotLUISClassification 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
978-1-4503-7513-9
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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