From Disagreement to Unity: A Cascade Perspective Network for Subjective Tasks

Tong Zhang, Canhui Zhang, Shuzhen Li, C. L. Philip Chen

Abstract: Subjective tasks involve annotating instances according to personal opinions, emotions, and feelings. The neural network model must learn about human thought and expression complexity. Handling instances with different annotation opinions is the main challenge in subjective tasks. Existing methods focus on majority voting or integrating opinions from a few assigned annotators, leading to biased decisions and limited performance. To address this issue, this article proposes a cascade perspective network (CPNet) to uncover reliable disagreement for subjective tasks. Specifically, CPNet learns each annotator’s personalized knowledge from annotation disagreement and stores them in an annotator bank through the personal perspective module for abundant disagreement information. Then, CPNet obtains consistent opinions by referring to all annotators’ opinions from the annotator bank through the comprehensive perspective module to reduce bias caused by noise. CPNet improves decision-making by considering the diversity and comprehensiveness of all annotators’ opinions. Moreover, it performs well in subjective tasks with limited or numerous annotators. The state-of-the-art (SOTA) results on subjective datasets from different domains demonstrate the effectiveness and generalizability of CPNet.

External IDs:dblp:journals/tcss/ZhangZLC25