Elisabeth Lex


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mCPT at SemEval-2023 Task 3: Multilingual Label-Aware Contrastive Pre-Training of Transformers for Few- and Zero-shot Framing Detection
Markus Reiter-Haas | Alexander Ertl | Kevin Innerhofer | Elisabeth Lex
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

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.


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Using Factual Density to Measure Informativeness of Web Documents
Christopher Horn | Alisa Zhila | Alexander Gelbukh | Roman Kern | Elisabeth Lex
Proceedings of the 19th Nordic Conference of Computational Linguistics (NODALIDA 2013)