# Dialog Intent Induction via Density-based Deep Clustering Ensemble

arXiv: https://arxiv.org/abs/2201.06731 · HTML: https://mlsanity.com/paper/2201.06731/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 18 Jan 2022.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction](https://mlsanity.com/paper/1909.02027/) (2019)
- [Benchmarking Natural Language Understanding Services for building Conversational Agents](https://mlsanity.com/paper/1903.05566/) (2019)
- [Multi-class Classification without Multi-class Labels](https://mlsanity.com/paper/1901.00544/) (2019)
- [On the Sentence Embeddings from Pre-trained Language Models](https://mlsanity.com/paper/2011.05864/) (2020)
- [Discovering New Intents via Constrained Deep Adaptive Clustering with Cluster Refinement](https://mlsanity.com/paper/1911.08891/) (2019)
- [Intent Mining from past conversations for conversational agent](https://mlsanity.com/paper/2005.11014/) (2020)
- [Dialog Intent Induction with Deep Multi-View Clustering](https://mlsanity.com/paper/1908.11487/) (2019)
- [Semi-supervised Clustering for Short Text via Deep Representation Learning](https://mlsanity.com/paper/1602.06797/) (2016)

## Built on by (most cited first, 2 of 2)

- [LANID: LLM-assisted New Intent Discovery](https://mlsanity.com/paper/2503.23740/) (2025)
- [CATCH: A Controllable Theme Detection Framework with Contextualized Clustering and Hierarchical Generation](https://mlsanity.com/paper/2512.21715/) (2025)

## Abstract (arXiv)

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.