# Dynamic Memory Induction Networks for Few-Shot Text Classification

arXiv: https://arxiv.org/abs/2005.05727 · HTML: https://mlsanity.com/paper/2005.05727/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2020.

## Built on by (most cited first, 23 of 23)

- [A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2205.06743/) (2022)
- [Duplex Conversation: Towards Human-like Interaction in Spoken Dialogue Systems](https://mlsanity.com/paper/2205.15060/) (2022)
- [Meta Learning for Natural Language Processing: A Survey](https://mlsanity.com/paper/2205.01500/) (2022)
- [Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text Classification](https://mlsanity.com/paper/2107.12262/) (2021)
- [Label Semantic Aware Pre-training for Few-shot Text Classification](https://mlsanity.com/paper/2204.07128/) (2022)
- [ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification](https://mlsanity.com/paper/2305.09269/) (2023)
- [Knowledge-Aware Meta-learning for Low-Resource Text Classification](https://mlsanity.com/paper/2109.04707/) (2021)
- [An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling](https://mlsanity.com/paper/2110.13691/) (2021)
- [CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation](https://mlsanity.com/paper/2211.11617/) (2022)
- [SpanProto: A Two-stage Span-based Prototypical Network for Few-shot Named Entity Recognition](https://mlsanity.com/paper/2210.09049/) (2022)
- [PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor Search](https://mlsanity.com/paper/2305.12217/) (2023)
- [Self-Evolution Learning for Mixup: Enhance Data Augmentation on Few-Shot Text Classification Tasks](https://mlsanity.com/paper/2305.13547/) (2023)
- [MCML: A Novel Memory-based Contrastive Meta-Learning Method for Few Shot Slot Tagging](https://mlsanity.com/paper/2108.11635/) (2021)
- [Meta-Tsallis-Entropy Minimization: A New Self-Training Approach for Domain Adaptation on Text Classification](https://mlsanity.com/paper/2308.02746/) (2023)
- [TART: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformation](https://mlsanity.com/paper/2306.02175/) (2023)
- [Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation](https://mlsanity.com/paper/2106.02960/) (2021)
- [Disentangling Task Relations for Few-shot Text Classification via Self-Supervised Hierarchical Task Clustering](https://mlsanity.com/paper/2211.08588/) (2022)
- [Few-Shot Electronic Health Record Coding through Graph Contrastive Learning](https://mlsanity.com/paper/2106.15467/) (2021)
- [Unsupervised Meta Learning for One Shot Title Compression in Voice Commerce](https://mlsanity.com/paper/2102.10760/) (2021)
- [A Neural Conversation Generation Model via Equivalent Shared Memory Investigation](https://mlsanity.com/paper/2108.09164/) (2021)
- [Tracking Brand-Associated Polarity-Bearing Topics in User Reviews](https://mlsanity.com/paper/2301.07183/) (2023)
- [TransPrompt v2: A Transferable Prompting Framework for Cross-task Text Classification](https://mlsanity.com/paper/2308.15010/) (2023)
- [Joint Input and Output Coordination for Class-Incremental Learning](https://mlsanity.com/paper/2409.05620/) (2024)

## Abstract (arXiv)

This paper proposes Dynamic Memory Induction Networks (DMIN) for few-shot text classification. The model utilizes dynamic routing to provide more flexibility to memory-based few-shot learning in order to better adapt the support sets, which is a critical capacity of few-shot classification models. Based on that, we further develop induction models with query information, aiming to enhance the generalization ability of meta-learning. The proposed model achieves new state-of-the-art results on the miniRCV1 and ODIC dataset, improving the best performance (accuracy) by 2~4%. Detailed analysis is further performed to show the effectiveness of each component.