ALPET: Active Few-shot Learning for Citation Worthiness Detection in Low-Resource Wikipedia Languages

TL;DR

ALPET is a novel framework combining active learning and pattern-exploiting training that significantly improves citation worthiness detection in low-resource Wikipedia languages with minimal labeled data.

Contribution

It introduces ALPET, a new approach that enhances CWD performance in low-resource languages using active learning and PET, reducing labeling effort.

Findings

ALPET outperforms the CCW baseline in Catalan, Basque, and Albanian datasets.

Performance plateaus after 300 labeled samples, indicating efficiency in low-resource scenarios.

Random sampling remains a strong baseline despite advanced query strategies.

Abstract

Citation Worthiness Detection (CWD) consists in determining which sentences, within an article or collection, should be backed up with a citation to validate the information it provides. This study, introduces ALPET, a framework combining Active Learning (AL) and Pattern-Exploiting Training (PET), to enhance CWD for languages with limited data resources. Applied to Catalan, Basque, and Albanian Wikipedia datasets, ALPET outperforms the existing CCW baseline while reducing the amount of labeled data in some cases above 80\%. ALPET's performance plateaus after 300 labeled samples, showing it suitability for low-resource scenarios where large, labeled datasets are not common. While specific active learning query strategies, like those employing K-Means clustering, can offer advantages, their effectiveness is not universal and often yields marginal gains over random sampling, particularly with smaller datasets. This suggests that random sampling, despite its simplicity, remains a strong baseline for CWD in constraint resource environments. Overall, ALPET's ability to achieve high performance with fewer labeled samples makes it a promising tool for enhancing the verifiability of online content in low-resource language settings.