Few-shot text classification aims to classify the text under the few-shot
scenario. Most of the previous methods adopt optimization-based meta learning
to obtain task distribution. However, due to the neglect of matching between
the few amount of samples and complicated models, as well as the distinction
between useful and useless task features, these methods suffer from the
overfitting issue. To address this issue, we propose a novel Adaptive
Meta-learner via Gradient Similarity (AMGS) method to improve the model
generalization ability to a new task. Specifically, the proposed AMGS
alleviates the overfitting based on two aspects: (i) acquiring the potential
semantic representation of samples and improving model generalization through
the self-supervised auxiliary task in the inner loop, (ii) leveraging the
adaptive meta-learner via gradient similarity to add constraints on the
gradient obtained by base-learner in the outer loop. Moreover, we make a
systematic analysis of the influence of regularization on the entire framework.
Experimental results on several benchmarks demonstrate that the proposed AMGS
consistently improves few-shot text classification performance compared with
the state-of-the-art optimization-based meta-learning approaches.

提出了一种基于自适应元学习和梯度相似性的方法 AMGS，通过获得样本的潜在语义表示和构建自监督辅助任务以及利用自适应元学习者对基学习者在外循环中获得梯度的约束，以解决少样本学习场景下优化方法的过拟合问题。实验结果表明，与基于优化方法的元学习方法相比，该方法能够提高 few-shot 文本分类的性能。