Unseen Target Stance Detection with Adversarial Domain Generalization
This addresses the challenge of domain differences between targets in stance detection for NLP applications, representing an incremental improvement.
The paper tackled the problem of unseen target stance detection by incorporating attention-based conditional encoding with adversarial domain generalization, achieving new state-of-the-art performance on the SemEval-2016 dataset.
Although stance detection has made great progress in the past few years, it is still facing the problem of unseen targets. In this study, we investigate the domain difference between targets and thus incorporate attention-based conditional encoding with adversarial domain generalization to perform unseen target stance detection. Experimental results show that our approach achieves new state-of-the-art performance on the SemEval-2016 dataset, demonstrating the importance of domain difference between targets in unseen target stance detection.