Self-Training for Sample-Efficient Active Learning for Text Classification with Pre-Trained Language Models

TL;DR

This paper explores how self-training can enhance the efficiency of active learning in text classification tasks using pre-trained language models, achieving comparable results with significantly less labeled data.

Contribution

It introduces HAST, a novel self-training strategy that improves active learning efficiency, and provides a comprehensive evaluation of self-training approaches in NLP.

Findings

HAST outperforms previous self-training methods.

Achieves comparable results with only 25% of labeled data.

Effective across four text classification benchmarks.

Abstract

Active learning is an iterative labeling process that is used to obtain a small labeled subset, despite the absence of labeled data, thereby enabling to train a model for supervised tasks such as text classification. While active learning has made considerable progress in recent years due to improvements provided by pre-trained language models, there is untapped potential in the often neglected unlabeled portion of the data, although it is available in considerably larger quantities than the usually small set of labeled data. In this work, we investigate how self-training, a semi-supervised approach that uses a model to obtain pseudo-labels for unlabeled data, can be used to improve the efficiency of active learning for text classification. Building on a comprehensive reproduction of four previous self-training approaches, some of which are evaluated for the first time in the context of active learning or natural language processing, we introduce HAST, a new and effective self-training strategy, which is evaluated on four text classification benchmarks. Our results show that it outperforms the reproduced self-training approaches and reaches classification results comparable to previous experiments for three out of four datasets, using as little as 25% of the data. The code is publicly available at https://github.com/chschroeder/self-training-for-sample-efficient-active-learning .