# Dialog Intent Induction with Deep Multi-View Clustering

arXiv: https://arxiv.org/abs/1908.11487 · HTML: https://mlsanity.com/paper/1908.11487/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2019.

## Built on by (most cited first, 20 of 20)

- [Deep Clustering: A Comprehensive Survey](https://mlsanity.com/paper/2210.04142/) (2022)
- [New Intent Discovery with Pre-training and Contrastive Learning](https://mlsanity.com/paper/2205.12914/) (2022)
- [TEXTOIR: An Integrated and Visualized Platform for Text Open Intent Recognition](https://mlsanity.com/paper/2110.15063/) (2021)
- [User Satisfaction Estimation with Sequential Dialogue Act Modeling in Goal-oriented Conversational Systems](https://mlsanity.com/paper/2202.02912/) (2022)
- [Workflow Discovery from Dialogues in the Low Data Regime](https://mlsanity.com/paper/2205.11690/) (2022)
- [Intent Induction from Conversations for Task-Oriented Dialogue Track at DSTC 11](https://mlsanity.com/paper/2304.12982/) (2023)
- [Open Intent Discovery through Unsupervised Semantic Clustering and Dependency Parsing](https://mlsanity.com/paper/2104.12114/) (2021)
- [Multi-View Clustering for Open Knowledge Base Canonicalization](https://mlsanity.com/paper/2206.11130/) (2022)
- [NatCS: Eliciting Natural Customer Support Dialogues](https://mlsanity.com/paper/2305.03007/) (2023)
- [LANID: LLM-assisted New Intent Discovery](https://mlsanity.com/paper/2503.23740/) (2025)
- [Dialog Intent Induction via Density-based Deep Clustering Ensemble](https://mlsanity.com/paper/2201.06731/) (2022)
- [Convex Polytope Modelling for Unsupervised Derivation of Semantic Structure for Data-efficient Natural Language Understanding](https://mlsanity.com/paper/2201.10588/) (2022)
- [Analysis of Utterance Embeddings and Clustering Methods Related to Intent Induction for Task-Oriented Dialogue](https://mlsanity.com/paper/2212.02021/) (2022)
- [Multi-Stage Coarse-to-Fine Contrastive Learning for Conversation Intent Induction](https://mlsanity.com/paper/2303.05034/) (2023)
- [Towards a Fully Unsupervised Framework for Intent Induction in Customer Support Dialogues](https://mlsanity.com/paper/2307.15410/) (2023)
- [RSVP: Customer Intent Detection via Agent Response Contrastive and Generative Pre-Training](https://mlsanity.com/paper/2310.09773/) (2023)
- [IntentGrasp: A Comprehensive Benchmark for Intent Understanding](https://mlsanity.com/paper/2605.06832/) (2026)
- [DORIC : Domain Robust Fine-Tuning for Open Intent Clustering through Dependency Parsing](https://mlsanity.com/paper/2303.09827/) (2023)
- [Multi-View Zero-Shot Open Intent Induction from Dialogues: Multi Domain Batch and Proxy Gradient Transfer](https://mlsanity.com/paper/2303.13099/) (2023)
- [CATCH: A Controllable Theme Detection Framework with Contextualized Clustering and Hierarchical Generation](https://mlsanity.com/paper/2512.21715/) (2025)

## Abstract (arXiv)

We introduce the dialog intent induction task and present a novel deep multi-view clustering approach to tackle the problem. Dialog intent induction aims at discovering user intents from user query utterances in human-human conversations such as dialogs between customer support agents and customers. Motivated by the intuition that a dialog intent is not only expressed in the user query utterance but also captured in the rest of the dialog, we split a conversation into two independent views and exploit multi-view clustering techniques for inducing the dialog intent. In particular, we propose alternating-view k-means (AV-KMEANS) for joint multi-view representation learning and clustering analysis. The key innovation is that the instance-view representations are updated iteratively by predicting the cluster assignment obtained from the alternative view, so that the multi-view representations of the instances lead to similar cluster assignments. Experiments on two public datasets show that AV-KMEANS can induce better dialog intent clusters than state-of-the-art unsupervised representation learning methods and standard multi-view clustering approaches.