# Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection

arXiv: https://arxiv.org/abs/2010.02481 · HTML: https://mlsanity.com/paper/2010.02481/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2020.

## Built on by (most cited first, 17 of 17)

- [Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System](https://mlsanity.com/paper/2104.11882/) (2021)
- [Effectiveness of Pre-training for Few-shot Intent Classification](https://mlsanity.com/paper/2109.05782/) (2021)
- [Fine-tuning Pre-trained Language Models for Few-shot Intent Detection: Supervised Pre-training and Isotropization](https://mlsanity.com/paper/2205.07208/) (2022)
- [CoF-CoT: Enhancing Large Language Models with Coarse-to-Fine Chain-of-Thought Prompting for Multi-domain NLU Tasks](https://mlsanity.com/paper/2310.14623/) (2023)
- [Pseudo Siamese Network for Few-shot Intent Generation](https://mlsanity.com/paper/2105.00896/) (2021)
- [Enhancing Cross-lingual Transfer via Phonemic Transcription Integration](https://mlsanity.com/paper/2307.04361/) (2023)
- [Prompting with Phonemes: Enhancing LLMs' Multilinguality for Non-Latin Script Languages](https://mlsanity.com/paper/2411.02398/) (2024)
- [Revisit Few-shot Intent Classification with PLMs: Direct Fine-tuning vs. Continual Pre-training](https://mlsanity.com/paper/2306.05278/) (2023)
- [Slot Induction via Pre-trained Language Model Probing and Multi-level Contrastive Learning](https://mlsanity.com/paper/2308.04712/) (2023)
- [CORI: CJKV Benchmark with Romanization Integration -- A step towards Cross-lingual Transfer Beyond Textual Scripts](https://mlsanity.com/paper/2404.12618/) (2024)
- [REIC: RAG-Enhanced Intent Classification at Scale](https://mlsanity.com/paper/2506.00210/) (2025)
- [The Devil is in the Details: On Models and Training Regimes for Few-Shot Intent Classification](https://mlsanity.com/paper/2210.06440/) (2022)
- [BIRD: Bronze Inscription Restoration and Dating](https://mlsanity.com/paper/2511.01589/) (2025)
- [MGIMN: Multi-Grained Interactive Matching Network for Few-shot Text Classification](https://mlsanity.com/paper/2204.04952/) (2022)
- [Prompt Learning With Knowledge Memorizing Prototypes For Generalized Few-Shot Intent Detection](https://mlsanity.com/paper/2309.04971/) (2023)
- [Exploring the Limits of Natural Language Inference Based Setup for Few-Shot Intent Detection](https://mlsanity.com/paper/2112.07434/) (2021)
- [Building a Few-Shot Cross-Domain Multilingual NLU Model for Customer Care](https://mlsanity.com/paper/2506.04389/) (2025)

## Abstract (arXiv)

Few-shot Intent Detection is challenging due to the scarcity of available annotated utterances. Although recent works demonstrate that multi-level matching plays an important role in transferring learned knowledge from seen training classes to novel testing classes, they rely on a static similarity measure and overly fine-grained matching components. These limitations inhibit generalizing capability towards Generalized Few-shot Learning settings where both seen and novel classes are co-existent. In this paper, we propose a novel Semantic Matching and Aggregation Network where semantic components are distilled from utterances via multi-head self-attention with additional dynamic regularization constraints. These semantic components capture high-level information, resulting in more effective matching between instances. Our multi-perspective matching method provides a comprehensive matching measure to enhance representations of both labeled and unlabeled instances. We also propose a more challenging evaluation setting that considers classification on the joint all-class label space. Extensive experimental results demonstrate the effectiveness of our method. Our code and data are publicly available.