mlsanityData to 7 Oct 2026

Dynamic Memory Induction Networks for Few-Shot Text Classification

arXiv 2005.05727 ↗First posted 12 May 2020cs.CL cs.LGcited by 23

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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.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 May 2020.

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Citing papers per year: 2021: 8; 2022: 7; 2023: 7; 2024: 1.

23 papers.

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  1. 2022A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunitiescited by 162
  2. 2022Duplex Conversation: Towards Human-like Interaction in Spoken Dialogue Systemscited by 28
  3. 2022Meta Learning for Natural Language Processing: A Surveycited by 26
  4. 2021Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text Classificationcited by 18
  5. 2022Label Semantic Aware Pre-training for Few-shot Text Classificationcited by 18
  6. 2023ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classificationcited by 17
  7. 2021Knowledge-Aware Meta-learning for Low-Resource Text Classificationcited by 10
  8. 2021An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Fillingcited by 9
  9. 2022CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluationcited by 9
  10. 2022SpanProto: A Two-stage Span-based Prototypical Network for Few-shot Named Entity Recognitioncited by 8
  11. 2023Self-Evolution Learning for Mixup: Enhance Data Augmentation on Few-Shot Text Classification Taskscited by 5
  12. 2021MCML: A Novel Memory-based Contrastive Meta-Learning Method for Few Shot Slot Taggingcited by 4
11 more
  1. 2023PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor Searchcited by 4
  2. 2023Meta-Tsallis-Entropy Minimization: A New Self-Training Approach for Domain Adaptation on Text Classificationcited by 4
  3. 2023TART: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformationcited by 3
  4. 2021Meta-Learning with Variational Semantic Memory for Word Sense Disambiguationcited by 2
  5. 2022Disentangling Task Relations for Few-shot Text Classification via Self-Supervised Hierarchical Task Clusteringcited by 2
  6. 2021Few-Shot Electronic Health Record Coding through Graph Contrastive Learningcited by 1
  7. 2021Unsupervised Meta Learning for One Shot Title Compression in Voice Commercecited by 0
  8. 2021A Neural Conversation Generation Model via Equivalent Shared Memory Investigationcited by 0
  9. 2023Tracking Brand-Associated Polarity-Bearing Topics in User Reviewscited by 0
  10. 2023TransPrompt v2: A Transferable Prompting Framework for Cross-task Text Classificationcited by 0
  11. 2024Joint Input and Output Coordination for Class-Incremental Learningcited by 0