{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mcpt-at-semeval-2023-task-3-multilingual","title":"mCPT at SemEval-2023 Task 3: Multilingual Label-Aware Contrastive Pre-Training of Transformers for Few- and Zero-shot Framing Detection","arxiv_id":"2303.09901","date":"2023-03-17","proceeding":null,"authors":["Markus Reiter-Haas","Alexander Ertl","Kevin Innerebner","Elisabeth Lex"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2303.09901v3","url_pdf":"https://arxiv.org/pdf/2303.09901v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"mcpt-at-semeval-2023-task-3-multilingual","repo_url":"https://github.com/socialcomplab/semeval23-mcpt","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}