It Takes Two Flints to Make a Fire: Multitask Learning of Neural Relation and Explanation Classifiers. (arXiv:2204.11424v4 [cs.CL] UPDATED)

We propose an explainable approach for relation extraction that mitigates the
tension between generalization and explainability by jointly training for the
two goals. Our approach uses a multi-task learning architecture, which jointly
trains a classifier for relation extraction, and a sequence model that labels
words in the context of the relation that explain the decisions of the relation
classifier. We also convert the model outputs to rules to bring global
explanations to this approach. This sequence model is trained using …

arxiv classifiers multitask learning