Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dialog Intent Induction via Density-based Deep Clustering Ensemble   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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The categories of this paper are: math
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The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
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The categories of this paper are: nlin
7
The categories of this paper are: hep-th
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The categories of this paper are: hep-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: chao-dyn
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The categories of this paper are: physics
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The categories of this paper are: atom-ph
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The categories of this paper are: eess
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The categories of this paper are: quant-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-ph
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The categories of this paper are: solv-int