Dialog Intent Induction via Density-based Deep Clustering Ensemble

• URL: http://arxiv.org/abs/2201.06731v1
• Date: Tue, 18 Jan 2022 04:13:26 GMT
• Title: Dialog Intent Induction via Density-based Deep Clustering Ensemble
• Authors: Jiashu Pu, Guandan Chen, Yongzhu Chang, Xiaoxi Mao
• Abstract summary: In real-life applications, it is crucial to occasionally induce novel dialog intents from the conversation logs to improve the user experience. We propose the Density-based Deep Clustering Ensemble (DDCE) method for dialog intent induction. Our proposed method is more effective in dealing with real-life scenarios where a large number of outliers exist.
• Score: 12.05997006407326
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing task-oriented chatbots heavily rely on spoken language understanding (SLU) systems to determine a user's utterance's intent and other key information for fulfilling specific tasks. In real-life applications, it is crucial to occasionally induce novel dialog intents from the conversation logs to improve the user experience. In this paper, we propose the Density-based Deep Clustering Ensemble (DDCE) method for dialog intent induction. Compared to existing K-means based methods, our proposed method is more effective in dealing with real-life scenarios where a large number of outliers exist. To maximize data utilization, we jointly optimize texts' representations and the hyperparameters of the clustering algorithm. In addition, we design an outlier-aware clustering ensemble framework to handle the overfitting issue. Experimental results over seven datasets show that our proposed method significantly outperforms other state-of-the-art baselines.

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