mlsanityData to 7 Oct 2026

Enhancing the Generalization for Intent Classification and Out-of-Domain Detection in SLU

arXiv 2106.14464 ↗First posted 28 Jun 2021cs.CL cs.AIcited by 22

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Abstract (arXiv)

Intent classification is a major task in spoken language understanding (SLU). Since most models are built with pre-collected in-domain (IND) training utterances, their ability to detect unsupported out-of-domain (OOD) utterances has a critical effect in practical use. Recent works have shown that using extra data and labels can improve the OOD detection performance, yet it could be costly to collect such data. This paper proposes to train a model with only IND data while supporting both IND intent classification and OOD detection. Our method designs a novel domain-regularized module (DRM) to reduce the overconfident phenomenon of a vanilla classifier, achieving a better generalization in both cases. Besides, DRM can be used as a drop-in replacement for the last layer in any neural network-based intent classifier, providing a low-cost strategy for a significant improvement. The evaluation on four datasets shows that our method built on BERT and RoBERTa models achieves state-of-the-art performance against existing approaches and the strong baselines we created for the comparisons.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 16 of its 40 references that are in the map. First posted on arXiv 28 Jun 2021.

Builds on

16 of 40 references.

References · 16

  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. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,201
  4. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,472
  5. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,325
  6. 2018Deep Anomaly Detection with Outlier Exposurecited by 939
  7. 2020Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Datacited by 408
  8. 2019An Evaluation Dataset for Intent Classification and Out-of-Scope Predictioncited by 378
  9. 2018Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfacescited by 376
  10. 2019BERT for Joint Intent Classification and Slot Fillingcited by 195
  11. 2016Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Fillingcited by 137
  12. 2019Out-of-domain Detection for Natural Language Understanding in Dialog Systemscited by 62
4 more
  1. 2018A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Fillingcited by 53
  2. 2019Out-of-Domain Detection for Low-Resource Text Classification Taskscited by 36
  3. 2018Neural Sentence Embedding using Only In-domain Sentences for Out-of-domain Sentence Detection in Dialog Systemscited by 18
  4. 2018OneNet: Joint Domain, Intent, Slot Prediction for Spoken Language Understandingcited by 13

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Citing papers per year: 2022: 7; 2023: 10; 2024: 3; 2025: 1; 2026: 1.

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  1. 2022Delving into Out-of-Distribution Detection with Vision-Language Representationscited by 215
  2. 2022State-of-the-art generalisation research in NLP: A taxonomy and reviewcited by 97
  3. 2023A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validationcited by 91
  4. 2022GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspectivecited by 72
  5. 2023Uncertainty in Natural Language Processing: Sources, Quantification, and Applicationscited by 44
  6. 2023A Survey on Out-of-Distribution Detection in NLPcited by 31
  7. 2023Is Fine-tuning Needed? Pre-trained Language Models Are Near Perfect for Out-of-Domain Detectioncited by 29
  8. 2022Learning Discriminative Representations and Decision Boundaries for Open Intent Detectioncited by 15
  9. 2023Fine-Tuning Deteriorates General Textual Out-of-Distribution Detection by Distorting Task-Agnostic Featurescited by 12
  10. 2023Exploring Large Language Models for Multi-Modal Out-of-Distribution Detectioncited by 12
  11. 2022Estimating Soft Labels for Out-of-Domain Intent Detectioncited by 11
  12. 2022Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer Ensemblecited by 9
10 more
  1. 2024Paraphrase and Aggregate with Large Language Models for Minimizing Intent Classification Errorscited by 9
  2. 2022A Simple Meta-learning Paradigm for Zero-shot Intent Classification with Mixture Attention Mechanismcited by 6
  3. 2023Out-of-Domain Intent Detection Considering Multi-Turn Dialogue Contextscited by 6
  4. 2025SimLabel: Consistency-Guided OOD Detection with Pretrained Vision-Language Modelscited by 5
  5. 2023Distinguish Sense from Nonsense: Out-of-Scope Detection for Virtual Assistantscited by 4
  6. 2024Domain Generalization via Causal Adjustment for Cross-Domain Sentiment Analysiscited by 3
  7. 2023Probing Out-of-Distribution Robustness of Language Models with Parameter-Efficient Transfer Learningcited by 2
  8. 2023Improving End-to-End Speech Processing by Efficient Text Data Utilization with Latent Synthesiscited by 0
  9. 2024One Stone, Four Birds: A Comprehensive Solution for QA System Using Supervised Contrastive Learningcited by 0
  10. 2026From Prediction to Justification: Aligning Sentiment Reasoning with Human Rationale via Reinforcement Learningcited by 0