Don't Blame the Data, Blame the Model: Understanding Noise and Bias When Learning from Subjective Annotations
<|reference_start|>Don't Blame the Data, Blame the Model: Understanding Noise and Bias When Learning from Subjective Annotations: 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.<|reference_end|>
@article{anand2024don't,
    title={Don't Blame the Data, Blame the Model: Understanding Noise and Bias When
  Learning from Subjective Annotations},
    author={Abhishek Anand, Negar Mokhberian, Prathyusha Naresh Kumar, Anweasha
  Saha, Zihao He, Ashwin Rao, Fred Morstatter, Kristina Lerman},
    journal={arXiv preprint arXiv:2403.04085},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.04085},
    primaryClass={cs.CL cs.CY}
}