mlsanityData to 7 Oct 2026

Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text Classification

arXiv 2107.12262 ↗First posted 26 Jul 2021cs.CL cs.AIcited by 18

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Abstract (arXiv)

Meta-learning has emerged as a trending technique to tackle few-shot text classification and achieved state-of-the-art performance. However, existing solutions heavily rely on the exploitation of lexical features and their distributional signatures on training data, while neglecting to strengthen the model's ability to adapt to new tasks. In this paper, we propose a novel meta-learning framework integrated with an adversarial domain adaptation network, aiming to improve the adaptive ability of the model and generate high-quality text embedding for new classes. Extensive experiments are conducted on four benchmark datasets and our method demonstrates clear superiority over the state-of-the-art models in all the datasets. In particular, the accuracy of 1-shot and 5-shot classification on the dataset of 20 Newsgroups is boosted from 52.1% to 59.6%, and from 68.3% to 77.8%, respectively.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 23 of its 33 references that are in the map. First posted on arXiv 26 Jul 2021.

Builds on

23 of 33 references.

References · 23

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  5. 2015Domain-Adversarial Training of Neural Networkscited by 3,023
  6. 2017Prototypical Networks for Few-shot Learningcited by 3,001
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  9. 2017Adversarial Discriminative Domain Adaptationcited by 1,309
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  11. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,139
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11 more
  1. 2016Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filteringcited by 517
  2. 2016FastText.zip: Compressing text classification modelscited by 331
  3. 2018Bayesian Model-Agnostic Meta-Learningcited by 194
  4. 2018Recasting Gradient-Based Meta-Learning as Hierarchical Bayescited by 180
  5. 2018Task-Agnostic Meta-Learning for Few-shot Learningcited by 151
  6. 2018Meta-Learning Probabilistic Inference For Predictioncited by 108
  7. 2019TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learningcited by 90
  8. 2019Induction Networks for Few-Shot Text Classificationcited by 64
  9. 2018Diverse Few-Shot Text Classification with Multiple Metricscited by 61
  10. 2019Few-shot Text Classification with Distributional Signaturescited by 58
  11. 2020Dynamic Memory Induction Networks for Few-Shot Text Classificationcited by 23

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Citing papers per year: 2022: 3; 2023: 11; 2024: 3; 2025: 1.

18 papers.

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  1. 2023Meta-learning approaches for few-shot learning: A survey of recent advancescited by 25
  2. 2022Imagination-Augmented Natural Language Understandingcited by 19
  3. 2023ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classificationcited by 17
  4. 2023Effective Structured Prompting by Meta-Learning and Representative Verbalizercited by 14
  5. 2023Model-Agnostic Meta-Learning for Multilingual Hate Speech Detectioncited by 11
  6. 2023MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learningcited by 8
  7. 2022A Simple Meta-learning Paradigm for Zero-shot Intent Classification with Mixture Attention Mechanismcited by 6
  8. 2023Boosting Few-Shot Text Classification via Distribution Estimationcited by 4
  9. 2023Improving Few-shot Generalization of Safety Classifiers via Data Augmented Parameter-Efficient Fine-Tuningcited by 4
  10. 2022Adaptive Meta-learner via Gradient Similarity for Few-shot Text Classificationcited by 3
  11. 2023TART: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformationcited by 3
  12. 2024Liberating Seen Classes: Boosting Few-Shot and Zero-Shot Text Classification via Anchor Generation and Classification Reframingcited by 3
6 more
  1. 2024Cross-Modal Augmentation for Few-Shot Multimodal Fake News Detectioncited by 3
  2. 2023CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain Adaptationcited by 2
  3. 2023Prompt Learning With Knowledge Memorizing Prototypes For Generalized Few-Shot Intent Detectioncited by 1
  4. 2023Meta-Learning Siamese Network for Few-Shot Text Classificationcited by 0
  5. 2024Improve Meta-learning for Few-Shot Text Classification with All You Can Acquire from the Taskscited by 0
  6. 2025MuSaRoNews: A Multidomain, Multimodal Satire Dataset from Romanian News Articlescited by 0