Cross-Target Stance Classification with Self-Attention Networks
Computation and Language
2018-07-12 v2 Artificial Intelligence
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.
Cite
@article{arxiv.1805.06593,
title = {Cross-Target Stance Classification with Self-Attention Networks},
author = {Chang Xu and Cecile Paris and Surya Nepal and Ross Sparks},
journal= {arXiv preprint arXiv:1805.06593},
year = {2018}
}
Comments
In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL2018)