Archaeology at BEA 2025 Shared Task: Are Simple Baselines Good Enough?
This paper describes our approach to the 5 classification tasks from the Building Educational Applications (BEA) 2025 Shared Task.Our methods range from classical machine learning models to fine-tuning large-scale transformer architectures.Despite the diversity of techniques, performance differences were often minor, suggesting the presence of strong surfacelevel signal in the data and a limiting effect of annotation noise -particularly around the "To some extent" label.Under lenient evaluation, simple models perform competitively, showing their effectiveness in low-resource settings.
Authors
- Sergiu Nisioi (ORCID: https://orcid.org/0000-0003-2247-4488)
- Ana Roșu
- Jany-Gabriel Ispas
Institutions
- University of Bucharest (RO)
- Language Science (South Korea) (KR)
Publication Details
- Published
- 2025-01-01
- DOI
- https://doi.org/10.18653/v1/2025.bea-1.98
- Primary Topic
- Image Processing and 3D Reconstruction
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Health Resources in Action
- Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii