Abstract
This paper describes our submission to the item difficulty prediction track of the BEA 2024 shared task. Our submission included the output of three systems: 1) a feature-based linear regression model, 2) a RoBERTa-based model and 3) a linear regression ensemble built on the predictions of the two previous models. Our systems ranked 7th, 8th and 5th respectively, demonstrating that simple models can achieve optimal results. A closer look at the results shows that predictions are more accurate for items in the middle of the difficulty range, with no other obvious relationships between difficulty and the accuracy of predictions.- Anthology ID:
- 2024.bea-1.42
- Volume:
- Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Ekaterina Kochmar, Marie Bexte, Jill Burstein, Andrea Horbach, Ronja Laarmann-Quante, Anaïs Tack, Victoria Yaneva, Zheng Yuan
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 503–511
- Language:
- URL:
- https://aclanthology.org/2024.bea-1.42
- DOI:
- Cite (ACL):
- Mariano Felice and Zeynep Duran Karaoz. 2024. The British Council submission to the BEA 2024 shared task. In Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024), pages 503–511, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- The British Council submission to the BEA 2024 shared task (Felice & Duran Karaoz, BEA 2024)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/2024.bea-1.42.pdf