Weakly-supervised text classification trains a classifier using the label name of each target class as the only supervision, which largely reduces human annotation efforts. Most existing methods first use the label names as static keyword-based features to generate pseudo labels, which are then used for final classifier training. While reasonable, such a commonly adopted framework suffers from two limitations: (1) keywords can have different meanings in different contexts and some text may not have any keyword, so keyword matching can induce noisy and inadequate pseudo labels; (2) the errors made in the pseudo label generation stage will directly propagate to the classifier training stage without a chance of being corrected. In this paper, we propose a new method, PIEClass, consisting of two modules: (1) a pseudo label acquisition module that uses zero-shot prompting of pre-trained language models (PLM) to get pseudo labels based on contextualized text understanding beyond static keyword matching, and (2) a noise-robust iterative ensemble training module that iteratively trains classifiers and updates pseudo labels by utilizing two PLM fine-tuning methods that regularize each other. Extensive experiments show that PIEClass achieves overall better performance than existing strong baselines on seven benchmark datasets and even achieves similar performance to fully-supervised classifiers on sentiment classification tasks.

翻译：弱监督文本分类仅利用每个目标类别的标签名称作为唯一监督信号来训练分类器，从而大幅减少人工标注工作量。现有方法通常先使用标签名称作为静态关键词特征生成伪标签，再用于最终分类器训练。虽然合理，但这种常见框架存在两个局限：（1）关键词在不同语境中可能具有不同含义且部分文本可能不含关键词，因此关键词匹配会产生噪声大且覆盖不足的伪标签；（2）伪标签生成阶段的错误会直接传播至分类器训练阶段而无法被纠正。本文提出新方法PIEClass，包含两个模块：（1）伪标签获取模块，利用预训练语言模型的零样本提示技术，超越静态关键词匹配，基于上下文文本理解获取伪标签；（2）抗噪迭代集成训练模块，通过两种相互正则化的预训练语言模型微调方法，迭代训练分类器并更新伪标签。大量实验表明，PIEClass在七个基准数据集上整体性能优于现有强基线方法，在情感分类任务中甚至达到与全监督分类器相当的性能。