Abstract
This paper describes our submission to theSemEval 2023 Task 3 on two subtasks: detectingpersuasion techniques and framing. Bothsubtasks are multi-label classification problems. We present a set of experiments, exploring howto get robust performance across languages usingpre-trained RoBERTa models. We test differentoversampling strategies, a strategy ofadding textual features from predictions obtainedwith related models, and present bothinconclusive and negative results. We achievea robust ranking across languages and subtaskswith our best ranking being nr. 1 for Subtask 3on Spanish.- Anthology ID:
- 2023.semeval-1.117
- Volume:
- Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Giovanni Da San Martino, Harish Tayyar Madabushi, Ritesh Kumar, Elisa Sartori
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 847–855
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.117
- DOI:
- 10.18653/v1/2023.semeval-1.117
- Cite (ACL):
- Amalie Pauli, Rafael Sarabia, Leon Derczynski, and Ira Assent. 2023. TeamAmpa at SemEval-2023 Task 3: Exploring Multilabel and Multilingual RoBERTa Models for Persuasion and Framing Detection. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 847–855, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- TeamAmpa at SemEval-2023 Task 3: Exploring Multilabel and Multilingual RoBERTa Models for Persuasion and Framing Detection (Pauli et al., SemEval 2023)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/2023.semeval-1.117.pdf