# Knowledge-Aware Meta-learning for Low-Resource Text Classification

arXiv: https://arxiv.org/abs/2109.04707 · HTML: https://mlsanity.com/paper/2109.04707/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 47 references that are in the map.
- First posted on arXiv 10 Sep 2021.

## Builds on (most cited first, 29 of 29)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Meta-SGD: Learning to Learn Quickly for Few-Shot Learning](https://mlsanity.com/paper/1707.09835/) (2017)
- [FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation](https://mlsanity.com/paper/1810.10147/) (2018)
- [Few-Shot Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1905.01723/) (2019)
- [Meta-Learning for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1808.08437/) (2018)
- [FewRel 2.0: Towards More Challenging Few-Shot Relation Classification](https://mlsanity.com/paper/1910.07124/) (2019)
- [TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning](https://mlsanity.com/paper/1905.06549/) (2019)
- [Hierarchically Structured Meta-learning](https://mlsanity.com/paper/1905.05301/) (2019)
- [Induction Networks for Few-Shot Text Classification](https://mlsanity.com/paper/1902.10482/) (2019)
- [Few-shot Text Classification with Distributional Signatures](https://mlsanity.com/paper/1908.06039/) (2019)
- [Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks](https://mlsanity.com/paper/1911.03863/) (2019)
- [Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Tasks](https://mlsanity.com/paper/2009.08445/) (2020)
- [Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs](https://mlsanity.com/paper/2007.02387/) (2020)
- [Automated Relational Meta-learning](https://mlsanity.com/paper/2001.00745/) (2020)
- [Dynamic Memory Induction Networks for Few-Shot Text Classification](https://mlsanity.com/paper/2005.05727/) (2020)
- [Neural Semantic Parsing in Low-Resource Settings with Back-Translation and Meta-Learning](https://mlsanity.com/paper/1909.05438/) (2019)
- [Physics-aware Spatiotemporal Modules with Auxiliary Tasks for Meta-Learning](https://mlsanity.com/paper/2006.08831/) (2020)
- [Meta Reasoning over Knowledge Graphs](https://mlsanity.com/paper/1908.04877/) (2019)
- [Inferential Text Generation with Multiple Knowledge Sources and Meta-Learning](https://mlsanity.com/paper/2004.03070/) (2020)

## Built on by (most cited first, 10 of 10)

- [MetaPrompting: Learning to Learn Better Prompts](https://mlsanity.com/paper/2209.11486/) (2022)
- [Detecting Spoilers in Movie Reviews with External Movie Knowledge and User Networks](https://mlsanity.com/paper/2304.11411/) (2023)
- [IDoFew: Intermediate Training Using Dual-Clustering in Language Models for Few Labels Text Classification](https://mlsanity.com/paper/2401.04025/) (2024)
- [Improving Graph-Based Text Representations with Character and Word Level N-grams](https://mlsanity.com/paper/2210.05999/) (2022)
- [Few-Shot Table-to-Text Generation with Prompt Planning and Knowledge Memorization](https://mlsanity.com/paper/2302.04415/) (2023)
- [Adapting Knowledge for Few-shot Table-to-Text Generation](https://mlsanity.com/paper/2302.12468/) (2023)
- [Retrieval-Augmented Meta Learning for Low-Resource Text Classification](https://mlsanity.com/paper/2309.04979/) (2023)
- [Low-Resource Multi-Granularity Academic Function Recognition Based on Multiple Prompt Knowledge](https://mlsanity.com/paper/2305.03287/) (2023)
- [LLM-based Contrastive Self-Supervised AMR Learning with Masked Graph Autoencoders for Fake News Detection](https://mlsanity.com/paper/2508.18819/) (2025)
- [CMHL: Contrastive Multi-Head Learning for Emotionally Consistent Text Classification](https://mlsanity.com/paper/2603.14078/) (2026)

## Abstract (arXiv)

Meta-learning has achieved great success in leveraging the historical learned knowledge to facilitate the learning process of the new task. However, merely learning the knowledge from the historical tasks, adopted by current meta-learning algorithms, may not generalize well to testing tasks when they are not well-supported by training tasks. This paper studies a low-resource text classification problem and bridges the gap between meta-training and meta-testing tasks by leveraging the external knowledge bases. Specifically, we propose KGML to introduce additional representation for each sentence learned from the extracted sentence-specific knowledge graph. The extensive experiments on three datasets demonstrate the effectiveness of KGML under both supervised adaptation and unsupervised adaptation settings.