
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.


coremsg

RulePrompt-Weakly-Supervised-Text-Classification-with-Prompting-PLMs-and-Self-Iterative-Logical-Rules


RulePrompt: Weakly Supervised Text Classification with Prompting PLMs and Self-Iterative Logical Rules


title_rewrite


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。


scarce-a-novel-approach-for-complementary-label-learning


SCARCE: A Novel Approach for Complementary-Label Learning



Introducing Reduced Labels for Long-Tailed Data to preserve supervised information and decrease labeling costs.


learning-from-reduced-labels-for-long-tailed-data-analysis


Learning from Reduced Labels for Long-Tailed Data Analysis



SCARCE proposes a consistent approach for complementary-label learning without relying on distribution assumptions, showing superior performance.


complementary-label-learning-without-assumptions-scarce-approach


Complementary Label Learning without Assumptions: SCARCE Approach
