# Out-of-Scope Intent Detection with Self-Supervision and Discriminative Training

arXiv: https://arxiv.org/abs/2106.08616 · HTML: https://mlsanity.com/paper/2106.08616/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 41 references that are in the map.
- First posted on arXiv 16 Jun 2021.

## Builds on (most cited first, 27 of 27)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization](https://mlsanity.com/paper/2006.16241/) (2020)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks](https://mlsanity.com/paper/1706.02690/) (2017)
- [Deep Anomaly Detection with Outlier Exposure](https://mlsanity.com/paper/1812.04606/) (2018)
- [Efficient Intent Detection with Dual Sentence Encoders](https://mlsanity.com/paper/2003.04807/) (2020)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples](https://mlsanity.com/paper/1711.09325/) (2017)
- [An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction](https://mlsanity.com/paper/1909.02027/) (2019)
- [Using Pre-Training Can Improve Model Robustness and Uncertainty](https://mlsanity.com/paper/1901.09960/) (2019)
- [On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks](https://mlsanity.com/paper/1905.11001/) (2019)
- [Pretrained Transformers Improve Out-of-Distribution Robustness](https://mlsanity.com/paper/2004.06100/) (2020)
- [DOC: Deep Open Classification of Text Documents](https://mlsanity.com/paper/1709.08716/) (2017)
- [Weakly-Supervised Neural Text Classification](https://mlsanity.com/paper/1809.01478/) (2018)
- [Deep Unknown Intent Detection with Margin Loss](https://mlsanity.com/paper/1906.00434/) (2019)
- [Deep Open Intent Classification with Adaptive Decision Boundary](https://mlsanity.com/paper/2012.10209/) (2020)
- [Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference](https://mlsanity.com/paper/2010.13009/) (2020)
- [Out-of-Domain Detection for Low-Resource Text Classification Tasks](https://mlsanity.com/paper/1909.05357/) (2019)
- [Neural Sentence Embedding using Only In-domain Sentences for Out-of-domain Sentence Detection in Dialog Systems](https://mlsanity.com/paper/1807.11567/) (2018)
- [Robustness properties of Facebook's ResNeXt WSL models](https://mlsanity.com/paper/1907.07640/) (2019)

## Built on by (most cited first, 30 of 42)

- [Delving into Out-of-Distribution Detection with Vision-Language Representations](https://mlsanity.com/paper/2211.13445/) (2022)
- [POEM: Out-of-Distribution Detection with Posterior Sampling](https://mlsanity.com/paper/2206.13687/) (2022)
- [A Survey on Out-of-Distribution Detection in NLP](https://mlsanity.com/paper/2305.03236/) (2023)
- [Is Fine-tuning Needed? Pre-trained Language Models Are Near Perfect for Out-of-Domain Detection](https://mlsanity.com/paper/2305.13282/) (2023)
- [How Good Are LLMs at Out-of-Distribution Detection?](https://mlsanity.com/paper/2308.10261/) (2023)
- [Learning Discriminative Representations and Decision Boundaries for Open Intent Detection](https://mlsanity.com/paper/2203.05823/) (2022)
- [Exploring Large Language Models for Multi-Modal Out-of-Distribution Detection](https://mlsanity.com/paper/2310.08027/) (2023)
- [Estimating Soft Labels for Out-of-Domain Intent Detection](https://mlsanity.com/paper/2211.05561/) (2022)
- [Multi-Level Knowledge Distillation for Out-of-Distribution Detection in Text](https://mlsanity.com/paper/2211.11300/) (2022)
- [Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer Ensemble](https://mlsanity.com/paper/2210.11034/) (2022)
- [IntentGPT: Few-shot Intent Discovery with Large Language Models](https://mlsanity.com/paper/2411.10670/) (2024)
- [Mysteries of the Deep: Role of Intermediate Representations in Out of Distribution Detection](https://mlsanity.com/paper/2510.05782/) (2025)
- [A Simple Meta-learning Paradigm for Zero-shot Intent Classification with Mixture Attention Mechanism](https://mlsanity.com/paper/2206.02179/) (2022)
- [Out-of-Domain Intent Detection Considering Multi-Turn Dialogue Contexts](https://mlsanity.com/paper/2305.03237/) (2023)
- [Deep Learning Approaches for Multimodal Intent Recognition: A Survey](https://mlsanity.com/paper/2507.22934/) (2025)
- [Learning to Classify Open Intent via Soft Labeling and Manifold Mixup](https://mlsanity.com/paper/2204.07804/) (2022)
- [Conformal Intent Classification and Clarification for Fast and Accurate Intent Recognition](https://mlsanity.com/paper/2403.18973/) (2024)
- [Distinguish Sense from Nonsense: Out-of-Scope Detection for Virtual Assistants](https://mlsanity.com/paper/2301.06544/) (2023)
- [Dual-Adapter: Training-free Dual Adaptation for Few-shot Out-of-Distribution Detection](https://mlsanity.com/paper/2405.16146/) (2024)
- [A new approach for fine-tuning sentence transformers for intent classification and out-of-scope detection tasks](https://mlsanity.com/paper/2410.13649/) (2024)
- [Exploring the Vulnerability of the Content Moderation Guardrail in Large Language Models via Intent Manipulation](https://mlsanity.com/paper/2505.18556/) (2025)
- [Learning Better Intent Representations for Financial Open Intent Classification](https://mlsanity.com/paper/2210.14304/) (2022)
- [Effective Open Intent Classification with K-center Contrastive Learning and Adjustable Decision Boundary](https://mlsanity.com/paper/2304.10220/) (2023)
- [SELFOOD: Self-Supervised Out-Of-Distribution Detection via Learning to Rank](https://mlsanity.com/paper/2305.14696/) (2023)
- [Out-of-Distribution Detection through Soft Clustering with Non-Negative Kernel Regression](https://mlsanity.com/paper/2407.13141/) (2024)
- [A Pointer Network-based Approach for Joint Extraction and Detection of Multi-Label Multi-Class Intents](https://mlsanity.com/paper/2410.22476/) (2024)
- [LANID: LLM-assisted New Intent Discovery](https://mlsanity.com/paper/2503.23740/) (2025)
- [The Open-World Lottery Ticket Hypothesis for OOD Intent Classification](https://mlsanity.com/paper/2210.07071/) (2022)
- [A Hybrid Architecture for Out of Domain Intent Detection and Intent Discovery](https://mlsanity.com/paper/2303.04134/) (2023)
- [Universal Domain Adaptation for Robust Handling of Distributional Shifts in NLP](https://mlsanity.com/paper/2310.14849/) (2023)

## Abstract (arXiv)

Out-of-scope intent detection is of practical importance in task-oriented dialogue systems. Since the distribution of outlier utterances is arbitrary and unknown in the training stage, existing methods commonly rely on strong assumptions on data distribution such as mixture of Gaussians to make inference, resulting in either complex multi-step training procedures or hand-crafted rules such as confidence threshold selection for outlier detection. In this paper, we propose a simple yet effective method to train an out-of-scope intent classifier in a fully end-to-end manner by simulating the test scenario in training, which requires no assumption on data distribution and no additional post-processing or threshold setting. Specifically, we construct a set of pseudo outliers in the training stage, by generating synthetic outliers using inliner features via self-supervision and sampling out-of-scope sentences from easily available open-domain datasets. The pseudo outliers are used to train a discriminative classifier that can be directly applied to and generalize well on the test task. We evaluate our method extensively on four benchmark dialogue datasets and observe significant improvements over state-of-the-art approaches. Our code has been released at https://github.com/liam0949/DCLOOS.