mlsanityData to 7 Oct 2026

Intent Induction from Conversations for Task-Oriented Dialogue Track at DSTC 11

arXiv 2304.12982 ↗First posted 25 Apr 2023cs.CLcited by 10

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Abstract (arXiv)

With increasing demand for and adoption of virtual assistants, recent work has investigated ways to accelerate bot schema design through the automatic induction of intents or the induction of slots and dialogue states. However, a lack of dedicated benchmarks and standardized evaluation has made progress difficult to track and comparisons between systems difficult to make. This challenge track, held as part of the Eleventh Dialog Systems Technology Challenge, introduces a benchmark that aims to evaluate methods for the automatic induction of customer intents in a realistic setting of customer service interactions between human agents and customers. We propose two subtasks for progressively tackling the automatic induction of intents and corresponding evaluation methodologies. We then present three datasets suitable for evaluating the tasks and propose simple baselines. Finally, we summarize the submissions and results of the challenge track, for which we received submissions from 34 teams.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 27 of its 56 references that are in the map. First posted on arXiv 25 Apr 2023.

Builds on

27 of 56 references.

References · 27

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  4. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  5. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,854
  6. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
  7. 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,168
  8. 2020MPNet: Masked and Permuted Pre-training for Language Understandingcited by 973
  9. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 779
  10. 2015Unsupervised Deep Embedding for Clustering Analysiscited by 763
  11. 2018MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modellingcited by 760
  12. 2019Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Datasetcited by 426
15 more
  1. 2020Efficient Intent Detection with Dual Sentence Encoderscited by 419
  2. 2019An Evaluation Dataset for Intent Classification and Out-of-Scope Predictioncited by 378
  3. 2018Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfacescited by 376
  4. 2019Benchmarking Natural Language Understanding Services for building Conversational Agentscited by 166
  5. 2021Supporting Clustering with Contrastive Learningcited by 90
  6. 2021Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systemscited by 73
  7. 2020Discovering New Intents with Deep Aligned Clusteringcited by 68
  8. 2019Discovering New Intents via Constrained Deep Adaptive Clustering with Cluster Refinementcited by 56
  9. 2020Deep Open Intent Classification with Adaptive Decision Boundarycited by 56
  10. 2021Effectiveness of Pre-training for Few-shot Intent Classificationcited by 27
  11. 2020Intent Mining from past conversations for conversational agentcited by 25
  12. 2019Dialog Intent Induction with Deep Multi-View Clusteringcited by 20
  13. 2022Learning Dialogue Representations from Consecutive Utterancescited by 17
  14. 2021Automatic Intent-Slot Induction for Dialogue Systemscited by 13
  15. 2021Open Intent Discovery through Unsupervised Semantic Clustering and Dependency Parsingcited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 5; 2026: 2.

10 papers.

  1. 2024Dial-In LLM: Human-Aligned LLM-in-the-loop Intent Clustering for Customer Service Dialoguescited by 11
  2. 2024Dialog2Flow: Pre-training Soft-Contrastive Action-Driven Sentence Embeddings for Automatic Dialog Flow Extractioncited by 8
  3. 2023Pre-training Intent-Aware Encoders for Zero- and Few-Shot Intent Classificationcited by 5
  4. 2025SPILL: Domain-Adaptive Intent Clustering based on Selection and Pooling with Large Language Modelscited by 2
  5. 2025LLMs Enable Bag-of-Texts Representations for Short-Text Clusteringcited by 2
  6. 2026METRO: Towards Strategy Induction from Expert Dialogue Transcripts for Non-collaborative Dialoguescited by 2
  7. 2026IntentGrasp: A Comprehensive Benchmark for Intent Understandingcited by 1
  8. 2025Controllable Conversational Theme Detection Track at DSTC 12cited by 0
  9. 2025LUMI: Unsupervised Intent Clustering with Multiple Pseudo-Labelscited by 0
  10. 2025CATCH: A Controllable Theme Detection Framework with Contextualized Clustering and Hierarchical Generationcited by 0