Cross-Target Stance Classification with Self-Attention Networks

This work proposes a neural model that can apply what has been learned from a source target to a destination target, and shows that this model can find useful information shared between relevant targets which improves generalization in certain scenarios.

Abstract

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.