Transfer and active learning for dissonance detection: Addressing the rare-class challenge
This study explores transfer- and active learning solutions for rare-class problems, focusing on detecting cognitive dissonance in social media. We propose a probability-of-rare-class (PRC) approach for selecting samples and evaluate various acquisition strategies. We find that PRC effectively guides annotations and improves model accuracy, while specific transfer-learning sequences enhance initial performance but don't benefit subsequent active learning iterations.
Jan 1, 2023
Discourse relation embeddings: Representing the relations between discourse segments in social media
Jan 1, 2021