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Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning
Towards this goal, we propose to mitigate the loss of knowledge from the interference among the different knowledge sources, by developing a modular variant of the knowledge aggregation as a new zero-shot commonsense reasoning framework.
TrustAL: Trustworthy Active Learning using Knowledge Distillation
A traditional view of data acquisition is that, through iterations, knowledge from human labels and models is implicitly distilled to monotonically increase the accuracy and label consistency.
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