Imbalanced Dataset

This paper proposes a novel self-training scheme SIAST for imbalanced slot filling that aims to learn unbiased margins between slot classes while mitigating potential slot confusion, and adaptively samples pseudo-labelled data to balance the slot distribution of the training set.