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Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks

arXiv 1908.10423 ↗First posted 27 Aug 2019cs.CLcited by 48

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Abstract (arXiv)

Learning general representations of text is a fundamental problem for many natural language understanding (NLU) tasks. Previously, researchers have proposed to use language model pre-training and multi-task learning to learn robust representations. However, these methods can achieve sub-optimal performance in low-resource scenarios. Inspired by the recent success of optimization-based meta-learning algorithms, in this paper, we explore the model-agnostic meta-learning algorithm (MAML) and its variants for low-resource NLU tasks. We validate our methods on the GLUE benchmark and show that our proposed models can outperform several strong baselines. We further empirically demonstrate that the learned representations can be adapted to new tasks efficiently and effectively.

Key facts. Cited by 48 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Aug 2019.

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Citing papers per year: 2021: 22; 2022: 12; 2023: 7; 2024: 4; 2025: 2; 2026: 1.

48 papers.

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  1. 2021Meta-learning via Language Model In-context Tuningcited by 163
  2. 2021CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLPcited by 142
  3. 2021FLEX: Unifying Evaluation for Few-Shot NLPcited by 67
  4. 2023An Introduction to Bi-level Optimization: Foundations and Applications in Signal Processing and Machine Learningcited by 39
  5. 2021Controllable Semantic Parsing via Retrieval Augmentationcited by 35
  6. 2022Meta Learning for Natural Language Processing: A Surveycited by 26
  7. 2021MetaXL: Meta Representation Transformation for Low-resource Cross-lingual Learningcited by 21
  8. 2021ProtAugment: Unsupervised diverse short-texts paraphrasing for intent detection meta-learningcited by 18
  9. 2021Few Shot Dialogue State Tracking using Meta-learningcited by 16
  10. 2023CrossCodeBench: Benchmarking Cross-Task Generalization of Source Code Modelscited by 16
  11. 2021On sensitivity of meta-learning to support datacited by 15
  12. 2021Multilingual and cross-lingual document classification: A meta-learning approachcited by 14
36 more
  1. 2021Meta-Transfer Learning for Low-Resource Abstractive Summarizationcited by 14
  2. 2021Sign-MAML: Efficient Model-Agnostic Meta-Learning by SignSGDcited by 14
  3. 2022Meta-Learning the Difference: Preparing Large Language Models for Efficient Adaptationcited by 14
  4. 2024Better to Ask in English: Evaluation of Large Language Models on English, Low-resource and Cross-Lingual Settingscited by 14
  5. 2021Meta-Learning for Effective Multi-task and Multilingual Modellingcited by 13
  6. 2021Improving Both Domain Robustness and Domain Adaptability in Machine Translationcited by 11
  7. 2023Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?cited by 11
  8. 2021X-METRA-ADA: Cross-lingual Meta-Transfer Learning Adaptation to Natural Language Understanding and Question Answeringcited by 10
  9. 2021Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLPcited by 10
  10. 2022Continued Pretraining for Better Zero- and Few-Shot Promptabilitycited by 10
  11. 2021CLUES: Few-Shot Learning Evaluation in Natural Language Understandingcited by 9
  12. 2022Learning a Better Initialization for Soft Prompts via Meta-Learningcited by 9
  13. 2022EntityCS: Improving Zero-Shot Cross-lingual Transfer with Entity-Centric Code Switchingcited by 8
  14. 2023Bayes meets Bernstein at the Meta Level: an Analysis of Fast Rates in Meta-Learning with PAC-Bayescited by 8
  15. 2023Adversarial Robustness of Prompt-based Few-Shot Learning for Natural Language Understandingcited by 8
  16. 2021Few-shot Learning for Slot Tagging with Attentive Relational Networkcited by 6
  17. 2024ARAIDA: Analogical Reasoning-Augmented Interactive Data Annotationcited by 6
  18. 2022On the cross-lingual transferability of multilingual prototypical models across NLU taskscited by 4
  19. 2024MetaRM: Shifted Distributions Alignment via Meta-Learningcited by 4
  20. 2021Meta-Learning with MAML on Treescited by 2
  21. 2021Meta-Learning with Variational Semantic Memory for Word Sense Disambiguationcited by 2
  22. 2022m4Adapter: Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-Adaptercited by 2
  23. 2022Leveraging Open Data and Task Augmentation to Automated Behavioral Coding of Psychotherapy Conversations in Low-Resource Scenarioscited by 2
  24. 2022Disentangling Task Relations for Few-shot Text Classification via Self-Supervised Hierarchical Task Clusteringcited by 2
  25. 2023Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Financecited by 2
  26. 2023SPEC: Summary Preference Decomposition for Low-Resource Abstractive Summarizationcited by 1
  27. 2024A (More) Realistic Evaluation Setup for Generalisation of Community Models on Malicious Content Detectioncited by 1
  28. 2026GAMBIT: A Three-Mode Benchmark for Adversarial Robustness in Multi-Agent LLM Collectivescited by 1
  29. 2021Unsupervised Meta Learning for One Shot Title Compression in Voice Commercecited by 0
  30. 2021Domain Adaptation in Dialogue Systems using Transfer and Meta-Learningcited by 0
  31. 2021Cross-lingual Adaption Model-Agnostic Meta-Learning for Natural Language Understandingcited by 0
  32. 2022Persian Natural Language Inference: A Meta-learning approachcited by 0
  33. 2022Tackling Multiple Tasks with One Single Learning Frameworkcited by 0
  34. 2022Learning New Tasks from a Few Examples with Soft-Label Prototypescited by 0
  35. 2025State Space Models for Extractive Summarization in Low Resource Scenarioscited by 0
  36. 2025Valuable Hallucinations: Realizable Non-realistic Propositionscited by 0