This paper presents the winning system for the zero-shot Spanish framing
detection task, which also achieves competitive places in eight additional
languages. The challenge of the framing detection task lies in identifying a
set of 14 frames when only a few or zero samples are available, i.e., a
multilingual multi-label few- or zero-shot setting. Our developed solution
employs a pre-training procedure based on multilingual Transformers using a
label-aware contrastive loss function. In addition to describing the system, we
perform an embedding space analysis and ablation study to demonstrate how our
pre-training procedure supports framing detection to advance computational
framing analysis.

该论文介绍了零样本学习下多语种多标签框架检测任务的获胜系统，其采用多语种 Transformers 进行预训练，并使用有标签的对比损失函数。此外，作者进行了嵌入空间分析和消融研究，证明了他们的预训练程序如何支持框架检测，推动了计算框架分析的发展。