A Multi-Task Learning Framework for Multi-Target Stance Detection

Abstract

Multi-target stance detection aims to identify the stance taken toward a pair of different targets from the same text, and typically, there are multiple target pairs per dataset. Existing works generally train one model for each target pair. However, they fail to learn target-specific representations and are prone to overfitting. In this paper, we propose a new training strategy under the multi-task learning setting by training one model on all target pairs, which helps the model learn more universal representations and alleviate overfitting. Moreover, in order to extract more accurate target-specific representations, we propose a multi-task learning network which can jointly train our model with a stance (dis)agreement detection task that is designed to identify agreement and disagreement between stances in paired texts. Experimental results demonstrate that our proposed model outperforms the best-performing baseline by 12.39% in macro-averaged F1-score. Our resources are publicly available on GitHub.