We present the novel approach for stance detection across domains and targets, Metric Learning-Based Few-Shot Learning for Cross-Target and Cross-Domain Stance Detection (MLSD). MLSD utilizes metric learning with triplet loss to capture semantic similarities and differences between stance targets, enhancing domain adaptation. By constructing a discriminative embedding space, MLSD allows a cross-target or cross-domain stance detection model to acquire useful examples from new target domains. We evaluate MLSD in multiple cross-target and cross-domain scenarios across two datasets, showing statistically significant improvement in stance detection performance across six widely used stance detection models.

翻译：我们提出了一种新颖的跨领域与跨目标立场检测方法，即基于度量学习的小样本学习用于跨目标与跨领域立场检测（MLSD）。MLSD利用度量学习与三元组损失来捕捉立场目标之间的语义相似性与差异，从而增强领域适应能力。通过构建一个判别性嵌入空间，MLSD使得跨目标或跨领域的立场检测模型能够从新的目标领域获取有用的示例。我们在两个数据集上的多种跨目标与跨领域场景中评估了MLSD，结果显示其在六种广泛使用的立场检测模型上的立场检测性能均取得了统计上显著的提升。