Spotting Spurious Data with Neural Networks

NAACL 2018 Hadi AmiriTimothy MillerGuergana Savova

Automatic identification of spurious instances (those with potentially wrong labels in datasets) can improve the quality of existing language resources, especially when annotations are obtained through crowdsourcing or automatically generated based on coded rankings. In this paper, we present effective approaches inspired by queueing theory and psychology of learning to automatically identify spurious instances in datasets... (read more)

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