# Induction Networks for Few-Shot Text Classification

arXiv: https://arxiv.org/abs/1902.10482 · HTML: https://mlsanity.com/paper/1902.10482/

## Key facts

- Cited by 64 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2019.

## Built on by (most cited first, 30 of 64)

- [Meta-learning via Language Model In-context Tuning](https://mlsanity.com/paper/2110.07814/) (2021)
- [State-of-the-art generalisation research in NLP: A taxonomy and review](https://mlsanity.com/paper/2210.03050/) (2022)
- [Advances and Challenges in Meta-Learning: A Technical Review](https://mlsanity.com/paper/2307.04722/) (2023)
- [CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning](https://mlsanity.com/paper/2109.07589/) (2021)
- [Few-shot Intent Classification and Slot Filling with Retrieved Examples](https://mlsanity.com/paper/2104.05763/) (2021)
- [SPACE-3: Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation](https://mlsanity.com/paper/2209.06664/) (2022)
- [Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System](https://mlsanity.com/paper/2104.11882/) (2021)
- [Duplex Conversation: Towards Human-like Interaction in Spoken Dialogue Systems](https://mlsanity.com/paper/2205.15060/) (2022)
- [Effectiveness of Pre-training for Few-shot Intent Classification](https://mlsanity.com/paper/2109.05782/) (2021)
- [An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labeling](https://mlsanity.com/paper/2109.13023/) (2021)
- [Meta Learning for Natural Language Processing: A Survey](https://mlsanity.com/paper/2205.01500/) (2022)
- [Mask-guided BERT for Few Shot Text Classification](https://mlsanity.com/paper/2302.10447/) (2023)
- [Decomposed Meta-Learning for Few-Shot Named Entity Recognition](https://mlsanity.com/paper/2204.05751/) (2022)
- [Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse Finetuning](https://mlsanity.com/paper/2311.03748/) (2023)
- [Imagination-Augmented Natural Language Understanding](https://mlsanity.com/paper/2204.08535/) (2022)
- [Fine-tuning Pre-trained Language Models for Few-shot Intent Detection: Supervised Pre-training and Isotropization](https://mlsanity.com/paper/2205.07208/) (2022)
- [ProtAugment: Unsupervised diverse short-texts paraphrasing for intent detection meta-learning](https://mlsanity.com/paper/2105.12995/) (2021)
- [Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text Classification](https://mlsanity.com/paper/2107.12262/) (2021)
- [Few-shot Named Entity Recognition with Entity-level Prototypical Network Enhanced by Dispersedly Distributed Prototypes](https://mlsanity.com/paper/2208.08023/) (2022)
- [ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification](https://mlsanity.com/paper/2305.09269/) (2023)
- [Semantic-aligned Fusion Transformer for One-shot Object Detection](https://mlsanity.com/paper/2203.09093/) (2022)
- [Cluster & Tune: Boost Cold Start Performance in Text Classification](https://mlsanity.com/paper/2203.10581/) (2022)
- [Few-shot learning for medical text: A systematic review](https://mlsanity.com/paper/2204.14081/) (2022)
- [Linguistically-Enriched and Context-Aware Zero-shot Slot Filling](https://mlsanity.com/paper/2101.06514/) (2021)
- [Prompt-Based Metric Learning for Few-Shot NER](https://mlsanity.com/paper/2211.04337/) (2022)
- [Knowledge-Aware Meta-learning for Low-Resource Text Classification](https://mlsanity.com/paper/2109.04707/) (2021)
- [CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation](https://mlsanity.com/paper/2211.11617/) (2022)
- [ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback](https://mlsanity.com/paper/2107.14035/) (2021)
- [Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation](https://mlsanity.com/paper/2201.11576/) (2022)
- [Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation](https://mlsanity.com/paper/2203.11670/) (2022)

## Abstract (arXiv)

Text classification tends to struggle when data is deficient or when it needs to adapt to unseen classes. In such challenging scenarios, recent studies have used meta-learning to simulate the few-shot task, in which new queries are compared to a small support set at the sample-wise level. However, this sample-wise comparison may be severely disturbed by the various expressions in the same class. Therefore, we should be able to learn a general representation of each class in the support set and then compare it to new queries. In this paper, we propose a novel Induction Network to learn such a generalized class-wise representation, by innovatively leveraging the dynamic routing algorithm in meta-learning. In this way, we find the model is able to induce and generalize better. We evaluate the proposed model on a well-studied sentiment classification dataset (English) and a real-world dialogue intent classification dataset (Chinese). Experiment results show that on both datasets, the proposed model significantly outperforms the existing state-of-the-art approaches, proving the effectiveness of class-wise generalization in few-shot text classification.