Dialog Intent Induction via Density-based Deep Clustering Ensemble
This addresses the need for robust intent induction in real-life chatbot applications, though it appears incremental as it builds on existing clustering methods.
The paper tackles the problem of inducing novel dialog intents from conversation logs to improve task-oriented chatbots, proposing the Density-based Deep Clustering Ensemble (DDCE) method, which significantly outperforms state-of-the-art baselines across seven datasets.
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.