Abstract

Researchers have raised awareness about the harms of aggregating labels especially in subjective tasks that naturally contain disagreements among human annotators. In this work we show that models that are only provided aggregated labels show low confidence on high-disagreement data instances. While previous studies consider such instances as mislabeled, we argue that the reason the high-disagreement text instances have been hard-to-learn is that the conventional aggregated models underperform in extracting useful signals from subjective tasks. Inspired by recent studies demonstrating the effectiveness of learning from raw annotations, we investigate classifying using Multiple Ground Truth (Multi-GT) approaches. Our experiments show an improvement of confidence for the high-disagreement instances.

Metadata

publication

arXiv preprint arXiv:2403.04085, 2024

year

2024

publication date

2024/3/6

authors

Abhishek Anand, Negar Mokhberian, Prathyusha Naresh Kumar, Anweasha Saha, Zihao He, Ashwin Rao, Fred Morstatter, Kristina Lerman

link

https://arxiv.org/abs/2403.04085

resource_link

https://arxiv.org/pdf/2403.04085

journal

arXiv preprint arXiv:2403.04085