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UoR at SemEval-2021 task 12: on crowd annotations: learning with disagreements to optimise crowd truth

Osei-Brefo, E., Markchom, T. and Liang, H. (2021) UoR at SemEval-2021 task 12: on crowd annotations: learning with disagreements to optimise crowd truth. In: SemEval-2021, 5-6 August 2021, Bangkok. (In Press)

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Abstract/Summary

Crowd sourcing has been ubiquitously used for annotating enormous collections of data. However, the major obstacles of using crowd-sourced labels are noise and errors from non-expert annotations. In this work, two approaches dealing with the noise and errors in crowd-sourced labels are proposed. The first approach uses Sharpness-Aware Minimization (SAM), an optimization technique robust to noisy labels. The other approach leverages a neural network layer called crowd layer specifically designed to learn from crowd-sourced annotations. According to the results, the proposed approaches can improve the performance of Wide Residual Network model and Multi-layer Perception model applied on two crowd-sourced datasets in image processing domain.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:97212

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