The authors propose RulePrompt, a novel approach that leverages logical rules to enhance the understanding of categories in weakly supervised text classification tasks using prompting PLMs.
提案されたSCARCEアプローチは、均一な分布仮定や通常のラベルトレーニングセットに依存せず、一貫性のある補完ラベル学習を実現します。
Introducing Reduced Labels for Long-Tailed Data to preserve supervised information and decrease labeling costs.
SCARCE proposes a consistent approach for complementary-label learning without relying on distribution assumptions, showing superior performance.