Zeynep Duran Karaoz


2024

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The British Council submission to the BEA 2024 shared task
Mariano Felice | Zeynep Duran Karaoz
Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024)

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.
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