Cross-Target Stance Classification with Self-Attention Networks
This work addresses the challenge of cross-target stance classification for NLP applications, but it appears incremental as it builds on existing neural methods.
The paper tackles the problem of generalizing stance classifiers across different targets, proposing a neural model that transfers learned information from a source to a destination target, showing improved generalization in certain scenarios.
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.