论文标题
具有对抗领域概括的看不见的目标姿态检测
Unseen Target Stance Detection with Adversarial Domain Generalization
论文作者
论文摘要
尽管在过去几年中，立场检测取得了长足的进步，但仍面临着看不见的目标的问题。在这项研究中，我们研究了目标之间的域差异，从而将基于注意力的条件编码与对抗结构域的概括一起进行，以执行看不见的目标立场检测。实验结果表明，我们的方法在Semeval-2016数据集上实现了新的最先进的性能，这表明目标靶标在看不见的目标姿态检测中的域差异的重要性。
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.
