mlsanityData to 7 Oct 2026

Instance Relation Learning Network with Label Knowledge Propagation for Few-shot Multi-label Intent Detection

arXiv 2510.07776 ↗First posted 9 Oct 2025cs.CL cs.LGcited by 0

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Abstract (arXiv)

Few-shot Multi-label Intent Detection (MID) is crucial for dialogue systems, aiming to detect multiple intents of utterances in low-resource dialogue domains. Previous studies focus on a two-stage pipeline. They first learn representations of utterances with multiple labels and then use a threshold-based strategy to identify multi-label results. However, these methods rely on representation classification and ignore instance relations, leading to error propagation. To solve the above issues, we propose a multi-label joint learning method for few-shot MID in an end-to-end manner, which constructs an instance relation learning network with label knowledge propagation to eliminate error propagation. Concretely, we learn the interaction relations between instances with class information to propagate label knowledge between a few labeled (support set) and unlabeled (query set) instances. With label knowledge propagation, the relation strength between instances directly indicates whether two utterances belong to the same intent for multi-label prediction. Besides, a dual relation-enhanced loss is developed to optimize support- and query-level relation strength to improve performance. Experiments show that we outperform strong baselines by an average of 9.54% AUC and 11.19% Macro-F1 in 1-shot scenarios.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 30 of its 46 references that are in the map. First posted on arXiv 9 Oct 2025.

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References · 30

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  4. 2020Few-shot Learning for Multi-label Intent Detectioncited by 20
  5. 2022Fine-tuning Pre-trained Language Models for Few-shot Intent Detection: Supervised Pre-training and Isotropizationcited by 19
  6. 2021Inferring Prototypes for Multi-Label Few-Shot Image Classification with Word Vector Guided Attentioncited by 15
  7. 2021Hybrid Graph Neural Networks for Few-Shot Learningcited by 15
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  10. 2022TO-FLOW: Efficient Continuous Normalizing Flows with Temporal Optimization adjoint with Moving Speedcited by 8
  11. 2022Label-enhanced Prototypical Network with Contrastive Learning for Multi-label Few-shot Aspect Category Detectioncited by 7
  12. 2023Joint Multiple Intent Detection and Slot Filling with Supervised Contrastive Learning and Self-Distillationcited by 7
  13. 2023MISCA: A Joint Model for Multiple Intent Detection and Slot Filling with Intent-Slot Co-Attentioncited by 7
  14. 2024Boosting Model Resilience via Implicit Adversarial Data Augmentationcited by 7
  15. 2022Few-shot Adaptation Works with UnpredicTable Datacited by 6
  16. 2022Label-Driven Denoising Framework for Multi-Label Few-Shot Aspect Category Detectioncited by 5
  17. 2023QAID: Question Answering Inspired Few-shot Intent Detectioncited by 5
  18. 2023All Labels Together: Low-shot Intent Detection with an Efficient Label Semantic Encoding Paradigmcited by 1

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