Recently, massive research has achieved significant development in improving the performance of rumor detection. However, identifying rumors in an invisible domain is still an elusive challenge. To address this issue, we propose an unsupervised cross-domain rumor detection model that enhances contrastive learning and cross-attention by label-aware learning to alleviate the domain shift. The model performs cross-domain feature alignment and enforces target samples to align with the corresponding prototypes of a given source domain. Moreover, we use a cross-attention mechanism on a pair of source data and target data with the same labels to learn domain-invariant representations. Because the samples in a domain pair tend to express similar semantic patterns, especially on the people’s attitudes (e.g., supporting or denying) towards the same category of rumors. In addition, we add a label-aware learning module as an enhancement component to learn the correlations between labels and instances during training and generate a better label distribution to replace the original one-hot label vector to guide the model training. At the same time, we use the label representation learned by the label learning module to guide the production of pseudo-label for the target samples. We conduct experiments on four groups of cross-domain datasets and show that our proposed model achieves state-of-the-art performance.

中文翻译：

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标签感知学习增强无监督跨域谣言检测


最近，大量研究在提高谣言检测的性能方面取得了重大进展。然而，在无形的领域中识别谣言仍然是一个难以捉摸的挑战。为了解决这个问题，我们提出了一种无监督的跨域谣言检测模型，该模型通过标签感知学习来增强对比学习和交叉注意力，以减轻域转移。该模型执行跨域特征对齐，并强制目标样本与给定源域的相应原型对齐。此外，我们对一对具有相同标签的源数据和目标数据使用交叉注意力机制来学习域不变表示。因为领域对中的样本往往表达相似的语义模式，尤其是人们对同一类别的谣言的态度（例如，支持或否认）。此外，我们还添加了标签感知学习模块作为增强组件，以学习训练过程中标签和实例之间的相关性，并生成更好的标签分布来替换原来的 one-hot 标签向量来指导模型训练。同时，我们使用标签学习模块学到的标签表示来指导目标样本的伪标签制作。我们在四组跨域数据集上进行了实验，并表明我们提出的模型实现了最先进的性能。