XPASC: Measuring Generalization in Weak Supervision by Explainability and Association

1 code implementation • 3 Jun 2022 • Luisa März, Ehsaneddin Asgari, Fabienne Braune, Franziska Zimmermann, Benjamin Roth

To verify this assumption, we introduce a novel method, XPASC (eXPlainability-Association SCore), for measuring the generalization of a model trained with a weakly supervised dataset.

KnowMAN: Weakly Supervised Multinomial Adversarial Networks

1 code implementation • EMNLP 2021 • Luisa März, Ehsaneddin Asgari, Fabienne Braune, Franziska Zimmermann, Benjamin Roth

The knowledge is captured in labeling functions, which detect certain regularities or patterns in the training samples and annotate corresponding labels for training.

Language Modelling Weakly-supervised Learning