论文标题
通过基于密度的深群集集合的对话意图诱导
Dialog Intent Induction via Density-based Deep Clustering Ensemble
论文作者
论文摘要
现有的面向任务的聊天机器人在很大程度上依赖口语理解（SLU）系统来确定用户的话语意图和其他关键信息，以实现特定任务。在现实生活中，至关重要的是，偶尔从对话日志中引起新颖的对话方式以改善用户体验。在本文中，我们提出了基于密度的深群集集合（DDCE）方法来诱导对话。与现有基于K-均值的方法相比，我们提出的方法在处理存在大量异常值的现实情况方面更有效。为了最大化数据利用，我们共同优化了群集算法的文本表示和超参数。此外，我们设计了一个异常值的聚类集合框架来处理过度拟合的问题。七个数据集的实验结果表明，我们提出的方法显着优于其他最先进的基线。
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.
