Abstract It is a fact that natural language processing (NLP) has become an integral part of daily life, with research outcomes being integrated into various everyday implementations. A significant portion of this success can reasonably be attributed to the architecture of transformers. In this context, text classification problems constitute a large part of ongoing research. Simultaneously, there is a growing demand for high-quality labeled textual data. The latter is becoming increasingly urgent with the rising complexity and size of models. Based on this, the present work investigates the integration of active learning strategies into text classification problems using transformer-based models from the BERT family. Through an extensive experimental framework involving 10 datasets and 7 different BERT-based classifiers, we demonstrate that the incorporation of active learning in the context of text classification can significantly reduce the need for labeled data during the fine-tuning procedures. Specifically, our experimental results illustrate that without sacrificing model effectiveness–as measured by various evaluation metrics–we can achieve at least a 50% reduction in the dataset size in 70% of cases. Additionally, we show that the size of the dataset plays a crucial role in maintaining high performance levels.
Karanikola et al. (Thu,) studied this question.