Noise Correction on Subjective Datasets

Uthman Jinadu; Yi Ding

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arXiv · 2311.00619

Noise Correction on Subjective Datasets

Uthman Jinadu·Yi Ding

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Abstract

Incorporating every annotator's perspective is crucial for unbiased data modeling. Annotator fatigue and changing opinions over time can distort dataset annotations. To combat this, we propose to learn a more accurate representation of diverse opinions by utilizing multitask learning in conjunction with loss-based label correction. We show that using our novel formulation, we can cleanly separate agreeing and disagreeing annotations. Furthermore, this method provides a controllable way to encourage or discourage disagreement. We demonstrate that this modification can improve prediction performance in a single or multi-annotator setting. Lastly, we show that this method remains robust to additional label noise that is applied to subjective data.

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BibTeX RIS

Uthman Jinadu, Yi Ding. 2023-11-01. Noise Correction on Subjective Datasets. https://arxiv.org/abs/2311.00619

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