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arXiv · 2201.06731

Dialog Intent Induction via Density-based Deep Clustering Ensemble

Jiashu Pu·Guandan Chen·Yongzhu Chang·Xiaoxi Mao
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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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Jiashu Pu, Guandan Chen, Yongzhu Chang, Xiaoxi Mao. 2022-01-18. Dialog Intent Induction via Density-based Deep Clustering Ensemble. https://arxiv.org/abs/2201.06731

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