mlsanityData to 14 Oct 2026

OutFlip: Generating Out-of-Domain Samples for Unknown Intent Detection with Natural Language Attack

arXiv 2105.05601 ↗First posted 12 May 2021cs.CL cs.LGcited by 2

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Abstract (arXiv)

Out-of-domain (OOD) input detection is vital in a task-oriented dialogue system since the acceptance of unsupported inputs could lead to an incorrect response of the system. This paper proposes OutFlip, a method to generate out-of-domain samples using only in-domain training dataset automatically. A white-box natural language attack method HotFlip is revised to generate out-of-domain samples instead of adversarial examples. Our evaluation results showed that integrating OutFlip-generated out-of-domain samples into the training dataset could significantly improve an intent classification model's out-of-domain detection performance.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 15 of its 26 references that are in the map. First posted on arXiv 12 May 2021.

Builds on

15 of 26 references.

References · 15

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3 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. 2018Joint Learning of Domain Classification and Out-of-Domain Detection with Dynamic Class Weighting for Satisficing False Acceptance Ratescited by 17

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Cited by year

Citing papers per year: 2022: 1; 2023: 1.

2 papers.

  1. 2023A Survey on Out-of-Distribution Detection in NLPcited by 31
  2. 2022Estimating Soft Labels for Out-of-Domain Intent Detectioncited by 11