mlsanityData to 7 Oct 2026

UniNL: Aligning Representation Learning with Scoring Function for OOD Detection via Unified Neighborhood Learning

arXiv 2210.10722 ↗First posted 19 Oct 2022cs.CLcited by 5

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Abstract (arXiv)

Detecting out-of-domain (OOD) intents from user queries is essential for avoiding wrong operations in task-oriented dialogue systems. The key challenge is how to distinguish in-domain (IND) and OOD intents. Previous methods ignore the alignment between representation learning and scoring function, limiting the OOD detection performance. In this paper, we propose a unified neighborhood learning framework (UniNL) to detect OOD intents. Specifically, we design a K-nearest neighbor contrastive learning (KNCL) objective for representation learning and introduce a KNN-based scoring function for OOD detection. We aim to align representation learning with scoring function. Experiments and analysis on two benchmark datasets show the effectiveness of our method.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 19 of its 35 references that are in the map. First posted on arXiv 19 Oct 2022.

Builds on

19 of 35 references.

References · 19

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7 more
  1. 2019Deep Unknown Intent Detection with Margin Losscited by 66
  2. 2019Out-of-domain Detection for Natural Language Understanding in Dialog Systemscited by 62
  3. 2020Deep Open Intent Classification with Adaptive Decision Boundarycited by 56
  4. 2021Modeling Discriminative Representations for Out-of-Domain Detection with Supervised Contrastive Learningcited by 42
  5. 2018Joint Learning of Domain Classification and Out-of-Domain Detection with Dynamic Class Weighting for Satisficing False Acceptance Ratescited by 17
  6. 2017Chat Detection in an Intelligent Assistant: Combining Task-oriented and Non-task-oriented Spoken Dialogue Systemscited by 8
  7. 2021Energy-based Unknown Intent Detection with Data Manipulationcited by 6

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Cited by year

Citing papers per year: 2023: 4; 2024: 1.

5 papers.

  1. 2023A Survey on Out-of-Distribution Detection in NLPcited by 31
  2. 2024Beyond the Known: Investigating LLMs Performance on Out-of-Domain Intent Detectioncited by 14
  3. 2023ChatGPT as Data Augmentation for Compositional Generalization: A Case Study in Open Intent Detectioncited by 9
  4. 2023Continual Generalized Intent Discovery: Marching Towards Dynamic and Open-world Intent Recognitioncited by 5
  5. 2023APP: Adaptive Prototypical Pseudo-Labeling for Few-shot OOD Detectioncited by 3