Dialog Intent Induction via Density-based Deep Clustering Ensemble

TL;DR

This paper introduces DDCE, a density-based deep clustering ensemble method that effectively induces new dialog intents from conversation logs, especially in scenarios with many outliers, improving over existing methods.

Contribution

The paper presents a novel density-based deep clustering ensemble approach for dialog intent induction that outperforms K-means based methods in real-world scenarios with outliers.

Findings

Significantly outperforms state-of-the-art baselines on seven datasets.

Effectively handles outliers in dialog intent induction.

Jointly optimizes text representations and clustering hyperparameters.

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.