用Microsoft LUIS训练聊天机器人:意图不平衡对预测精度的影响

Proceedings of the 25th International Conference on Intelligent User Interfaces Companion Pub Date : 2020-03-13 DOI:10.1145/3379336.3381494

Elayne Ruane, Robert Young, Anthony Ventresque

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引用次数: 4

摘要

微软的LUIS是一种用于训练聊天机器人的自然语言理解服务。话语训练集的不平衡可能导致路易斯模型预测用户查询的错误意图。我们讨论了这个问题和来自微软的训练建议，以提高路易斯的预测精度。我们对从两个现有数据集创建的三个训练集进行批量测试，以探索这些建议的有效性。

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Training a Chatbot with Microsoft LUIS: Effect of Intent Imbalance on Prediction Accuracy

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.

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Proceedings of the 25th International Conference on Intelligent User Interfaces Companion

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