Considering a conversation thread, rumour stance classification aims to identify the opinion (e.g. agree or disagree) of replies towards a target (rumour story). Although the target is expected to be an essential component in traditional stance classification, we show that rumour stance classification datasets contain a considerable amount of real-world data whose stance could be naturally inferred directly from the replies, contributing to the strong performance of the supervised models without awareness of the target. We find that current target-aware models underperform in cases where the context of the target is crucial. Finally, we propose a simple yet effective framework to enhance reasoning with the targets, achieving state-of-the-art performance on two benchmark datasets.

翻译：考虑一个对话线程，谣言立场分类旨在识别回复对目标（谣言故事）的立场（例如赞同或反对）。尽管目标被认为是传统立场分类中的关键组成部分，但我们发现谣言立场分类数据集包含大量真实数据，其立场可以直接从回复中自然推断出来，这导致监督模型在不了解目标的情况下仍能取得优异表现。我们注意到，在目标上下文至关重要的场景中，现有依赖目标的模型表现不佳。最后，我们提出一个简单而有效的框架来增强对目标的推理能力，在两个基准数据集上实现了最先进的性能。