# Learning to Classify Intents and Slot Labels Given a Handful of Examples

arXiv: https://arxiv.org/abs/2004.10793 · HTML: https://mlsanity.com/paper/2004.10793/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2020.

## Built on by (most cited first, 30 of 31)

- [FLEX: Unifying Evaluation for Few-Shot NLP](https://mlsanity.com/paper/2107.07170/) (2021)
- [Neural Data Augmentation via Example Extrapolation](https://mlsanity.com/paper/2102.01335/) (2021)
- [Few-shot Intent Classification and Slot Filling with Retrieved Examples](https://mlsanity.com/paper/2104.05763/) (2021)
- [Controllable Semantic Parsing via Retrieval Augmentation](https://mlsanity.com/paper/2110.08458/) (2021)
- [Label Semantic Aware Pre-training for Few-shot Text Classification](https://mlsanity.com/paper/2204.07128/) (2022)
- [Learning Dialogue Representations from Consecutive Utterances](https://mlsanity.com/paper/2205.13568/) (2022)
- [Linguistically-Enriched and Context-Aware Zero-shot Slot Filling](https://mlsanity.com/paper/2101.06514/) (2021)
- [An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling](https://mlsanity.com/paper/2110.13691/) (2021)
- [NLU++: A Multi-Label, Slot-Rich, Generalisable Dataset for Natural Language Understanding in Task-Oriented Dialogue](https://mlsanity.com/paper/2204.13021/) (2022)
- [S3-DST: Structured Open-Domain Dialogue Segmentation and State Tracking in the Era of LLMs](https://mlsanity.com/paper/2309.08827/) (2023)
- [Advancing Single and Multi-task Text Classification through Large Language Model Fine-tuning](https://mlsanity.com/paper/2412.08587/) (2024)
- [Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling](https://mlsanity.com/paper/2106.07343/) (2021)
- [Pre-training Intent-Aware Encoders for Zero- and Few-Shot Intent Classification](https://mlsanity.com/paper/2305.14827/) (2023)
- [ILLUMINER: Instruction-tuned Large Language Models as Few-shot Intent Classifier and Slot Filler](https://mlsanity.com/paper/2403.17536/) (2024)
- [MCML: A Novel Memory-based Contrastive Meta-Learning Method for Few Shot Slot Tagging](https://mlsanity.com/paper/2108.11635/) (2021)
- [Transfer-Free Data-Efficient Multilingual Slot Labeling](https://mlsanity.com/paper/2305.13528/) (2023)
- [Cross-lingual alignments of ELMo contextual embeddings](https://mlsanity.com/paper/2106.15986/) (2021)
- [Semi-Supervised Few-Shot Intent Classification and Slot Filling](https://mlsanity.com/paper/2109.08754/) (2021)
- [TOKEN is a MASK: Few-shot Named Entity Recognition with Pre-trained Language Models](https://mlsanity.com/paper/2206.07841/) (2022)
- [The Devil is in the Details: On Models and Training Regimes for Few-Shot Intent Classification](https://mlsanity.com/paper/2210.06440/) (2022)
- [Toward Open-domain Slot Filling via Self-supervised Co-training](https://mlsanity.com/paper/2303.13801/) (2023)
- [Towards Zero-Shot Frame Semantic Parsing with Task Agnostic Ontologies and Simple Labels](https://mlsanity.com/paper/2305.03793/) (2023)
- [Decoupling Representation and Knowledge for Few-Shot Intent Classification and Slot Filling](https://mlsanity.com/paper/2312.13495/) (2023)
- [Few-shot Named Entity Recognition with Cloze Questions](https://mlsanity.com/paper/2111.12421/) (2021)
- [Few-Shot NLU with Vector Projection Distance and Abstract Triangular CRF](https://mlsanity.com/paper/2112.04999/) (2021)
- [MGIMN: Multi-Grained Interactive Matching Network for Few-shot Text Classification](https://mlsanity.com/paper/2204.04952/) (2022)
- [Multi-Stage Coarse-to-Fine Contrastive Learning for Conversation Intent Induction](https://mlsanity.com/paper/2303.05034/) (2023)
- [Template-assisted Contrastive Learning of Task-oriented Dialogue Sentence Embeddings](https://mlsanity.com/paper/2305.14299/) (2023)
- [Introducing "Forecast Utterance" for Conversational Data Science](https://mlsanity.com/paper/2309.03877/) (2023)
- [A survey of joint intent detection and slot-filling models in natural language understanding](https://mlsanity.com/paper/2101.08091/) (2021)

## Abstract (arXiv)

Intent classification (IC) and slot filling (SF) are core components in most goal-oriented dialogue systems. Current IC/SF models perform poorly when the number of training examples per class is small. We propose a new few-shot learning task, few-shot IC/SF, to study and improve the performance of IC and SF models on classes not seen at training time in ultra low resource scenarios. We establish a few-shot IC/SF benchmark by defining few-shot splits for three public IC/SF datasets, ATIS, TOP, and Snips. We show that two popular few-shot learning algorithms, model agnostic meta learning (MAML) and prototypical networks, outperform a fine-tuning baseline on this benchmark. Prototypical networks achieves significant gains in IC performance on the ATIS and TOP datasets, while both prototypical networks and MAML outperform the baseline with respect to SF on all three datasets. In addition, we demonstrate that joint training as well as the use of pre-trained language models, ELMo and BERT in our case, are complementary to these few-shot learning methods and yield further gains.