Data to 14 Oct 2026

Deep Unknown Intent Detection with Margin Loss

arXiv 1906.00434 ↗First posted 2 Jun 2019cs.CL cs.LGcited by 66

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Abstract (arXiv)

Identifying the unknown (novel) user intents that have never appeared in the training set is a challenging task in the dialogue system. In this paper, we present a two-stage method for detecting unknown intents. We use bidirectional long short-term memory (BiLSTM) network with the margin loss as the feature extractor. With margin loss, we can learn discriminative deep features by forcing the network to maximize inter-class variance and to minimize intra-class variance. Then, we feed the feature vectors to the density-based novelty detection algorithm, local outlier factor (LOF), to detect unknown intents. Experiments on two benchmark datasets show that our method can yield consistent improvements compared with the baseline methods.

Key facts. Cited by 66 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Jun 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 14; 2022: 20; 2023: 18; 2024: 8; 2025: 3; 2026: 3.

66 papers.

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2021Revisiting Mahalanobis Distance for Transformer-Based Out-of-Domain Detectioncited by 70
2022UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognitioncited by 61
2021Modeling Discriminative Representations for Out-of-Domain Detection with Supervised Contrastive Learningcited by 42
2021Out-of-Scope Intent Detection with Self-Supervision and Discriminative Trainingcited by 42
2022MIntRec: A New Dataset for Multimodal Intent Recognitioncited by 41
2021Open-world Machine Learning: Applications, Challenges, and Opportunitiescited by 34
2023A Survey on Out-of-Distribution Detection in NLPcited by 31
2022Duplex Conversation: Towards Human-like Interaction in Spoken Dialogue Systemscited by 28
2024MIntRec2.0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversationscited by 26
2021TEXTOIR: An Integrated and Visualized Platform for Text Open Intent Recognitioncited by 24
2021GOLD: Improving Out-of-Scope Detection in Dialogues using Data Augmentationcited by 20
2022Learning Discriminative Representations and Decision Boundaries for Open Intent Detectioncited by 15

38 more

2022Towards Textual Out-of-Domain Detection without In-Domain Labelscited by 14
2024Beyond the Known: Investigating LLMs Performance on Out-of-Domain Intent Detectioncited by 14
2021Automatic Intent-Slot Induction for Dialogue Systemscited by 13
2023Speech-Text Dialog Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignmentcited by 13
2023Fine-Tuning Deteriorates General Textual Out-of-Distribution Detection by Distorting Task-Agnostic Featurescited by 12
2022Estimating Soft Labels for Out-of-Domain Intent Detectioncited by 11
2022Multi-Level Knowledge Distillation for Out-of-Distribution Detection in Textcited by 11
2022Generalized Intent Discovery: Learning from Open World Dialogue Systemcited by 10
2022Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer Ensemblecited by 9
2022Distribution Calibration for Out-of-Domain Detection with Bayesian Approximationcited by 7
2021Energy-based Unknown Intent Detection with Data Manipulationcited by 6
2022Holistic Sentence Embeddings for Better Out-of-Distribution Detectioncited by 6
2023Out-of-Domain Intent Detection Considering Multi-Turn Dialogue Contextscited by 6
2022Learning to Classify Open Intent via Soft Labeling and Manifold Mixupcited by 5
2022UniNL: Aligning Representation Learning with Scoring Function for OOD Detection via Unified Neighborhood Learningcited by 5
2023Decoupling Pseudo Label Disambiguation and Representation Learning for Generalized Intent Discoverycited by 5
2023Continual Generalized Intent Discovery: Marching Towards Dynamic and Open-world Intent Recognitioncited by 5
2024Conformal Intent Classification and Clarification for Fast and Accurate Intent Recognitioncited by 5
2023Distinguish Sense from Nonsense: Out-of-Scope Detection for Virtual Assistantscited by 4
2024A new approach for fine-tuning sentence transformers for intent classification and out-of-scope detection taskscited by 4
2022Learning Better Intent Representations for Financial Open Intent Classificationcited by 3
2023Effective Open Intent Classification with K-center Contrastive Learning and Adjustable Decision Boundarycited by 3
2023APP: Adaptive Prototypical Pseudo-Labeling for Few-shot OOD Detectioncited by 3
2021OutFlip: Generating Out-of-Domain Samples for Unknown Intent Detection with Natural Language Attackcited by 2
2021Novel Slot Detection: A Benchmark for Discovering Unknown Slot Types in the Task-Oriented Dialogue Systemcited by 2
2022A Framework to Generate High-Quality Datapoints for Multiple Novel Intent Detectioncited by 2
2022Disentangling Confidence Score Distribution for Out-of-Domain Intent Detection with Energy-Based Learningcited by 2
2023Probing Out-of-Distribution Robustness of Language Models with Parameter-Efficient Transfer Learningcited by 2
2023A Hybrid Architecture for Out of Domain Intent Detection and Intent Discoverycited by 2
2024Improved Out-of-Scope Intent Classification with Dual Encoding and Threshold-based Re-Classificationcited by 2
2024From Zero to Hero: Cold-Start Anomaly Detectioncited by 2
2024Multi-Granularity Open Intent Classification via Adaptive Granular-Ball Decision Boundarycited by 2
2021Out-of-Scope Domain and Intent Classification through Hierarchical Joint Modelingcited by 1
2021A Joint and Domain-Adaptive Approach to Spoken Language Understandingcited by 1
2022Building the Intent Landscape of Real-World Conversational Corpora with Extractive Question-Answering Transformerscited by 1
2022Contrastive Novelty-Augmented Learning: Anticipating Outliers with Large Language Modelscited by 1
2023Open World Classification with Adaptive Negative Samplescited by 1
2023Open Set Relation Extraction via Unknown-Aware Trainingcited by 1

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