基于梯度相似性的自适应元学习器在小样本文本分类中的应用
Adaptive Meta-learner via Gradient Similarity for Few-shot Text Classification
AI总结：
针对小样本文本分类中因样本与模型不匹配及特征区分不足导致的过拟合问题，提出基于梯度相似性的自适应元学习器（AMGS），通过内循环自监督辅助任务与外循环梯度约束提升模型泛化能力，实验证明其性能优于现有方法。
摘要：
小样本文本分类旨在在小样本场景下对文本进行分类。以往的大多数方法采用基于优化的元学习来获取任务分布。然而，由于忽视了少量样本与复杂模型之间的匹配问题，以及有用与无用任务特征之间的区分，这些方法面临过拟合问题。为解决此问题，我们提出了一种新颖的基于梯度相似性的自适应元学习器（AMGS）方法，以提升模型对新任务的泛化能力。具体而言，所提出的AMGS从两方面缓解过拟合：（i）在内循环中通过自监督辅助任务获取样本的潜在语义表示并提升模型泛化能力；（ii）在外循环中利用基于梯度相似性的自适应元学习器对基础学习器获取的梯度添加约束。此外，我们系统分析了正则化对整个框架的影响。在多个基准上的实验结果表明，与最先进的基于优化的元学习方法相比，所提出的AMGS持续提升了小样本文本分类性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
