Cross-Target Stance Classification with Self-Attention Networks
Chang Xu, Cécile L. Paris, Surya Nepal, Ross Stewart Sparks · 2018
In stance classification, the target on which the stance is made defines the boundary of the task, and a classifier is usually trained for prediction on the same target.In this work, we explore the potential for generalizing classifiers between different targets, and propose a neural model that can apply what has been learned from a source target to a destination target.We show that our model can find useful information shared between relevant targets which improves generalization in certain scenarios.