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

mCPT at SemEval-2023 Task 3: Multilingual Label-Aware Contrastive Pre-Training of Transformers for Few- and Zero-shot Framing Detection