Zero-shot stance detection involves predicting stances that have not previously been encountered by adapting models to learn transferable features by aligning the source and destination target spaces. The acquisition of transferable target-invariant features is crucial for zero-shot stance detection. This work proposes a stance detection technique that can effectively adapt to new unseen targets, and the essence lies in acquiring fine-grained and easy-to-migrate target-invariant features from multiple perspectives as transferable knowledge. Specifically, we first perform data augmentation by masking topic keywords to mitigate the target dependency introduced by topic keywords in the text. Then, to account for the diversity and granularity of the sample features, we leverage instance-wise contrastive learning to extract transferable meta-features from multiple perspectives. The meta-features bridge features migration from known targets to unseen targets by incorporating different viewpoints. Finally, we incorporate an attention mechanism to fuse the multi-perspective transferable features for predicting the stance of previously unseen targets. The experimental results demonstrate the superiority of our model over competitive baselines across four benchmark datasets.

中文翻译：

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基于多视角可转移特征融合的零样本姿态检测

零镜头姿态检测涉及通过调整源目标空间和目标目标空间来调整模型以学习可转移特征来预测以前未遇到过的姿态。获取可转移的目标不变特征对于零样本姿态检测至关重要。这项工作提出了一种能够有效适应新的未见目标的姿态检测技术，其本质在于从多个角度获取细粒度且易于迁移的目标不变特征作为可迁移知识。具体来说，我们首先通过屏蔽主题关键字来执行数据增强，以减轻文本中主题关键字引入的目标依赖性。然后，为了考虑样本特征的多样性和粒度，我们利用实例对比学习从多个角度提取可转移的元特征。元特征通过合并不同的观点来桥接从已知目标到未知目标的迁移。最后，我们采用了一种注意力机制来融合多视角可转移特征，以预测以前未见过的目标的立场。实验结果证明了我们的模型在四个基准数据集上优于竞争基线。