Cross-Target Stance Detection Via Refined Meta-Learning

Abstract

Cross-target stance detection (CTSD) aims to identify the stance of the text towards a target, where stance annotations are available for (though related but) different targets. Recently, models based on external semantic and emotion knowledge have been proposed for CTSD, achieving promising performance. However, such solutions rely on much external resources and harness only one source target, which is a waste of other available targets. To address the problem above, we propose a many-to-one CTSD model based on meta-learning. To make the most of meta-learning, we further refine it with a balanced and easy-to-hard learning pattern. Specifically, for multiple-target training, we feed the model according to the similarity among targets, and utilize two kinds of re-balanced strategies to deal with the imbalance in data. We conduct experiments on SemEval 2016 task 6, and results demonstrate that our method is effective and establishes a new state-of-the-art macro-f1 score for CTSD.