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.

本文提出基于密度的深度聚类集成方法（DDCE）进行对话意图诱导，相比于K-means方法，该方法更加有效，能够处理存在大量异常值的真实场景。在七个数据集上的实验结果表明，相较于其他最先进的基线方法，我们的方法显著优于其他

通过基于密度的深度聚类集成实现对话意图归纳