KUCST at CheckThat 2023: How good can we be with a generic model?
Manex Agirrezabal
In this paper we present our method for tasks 2 and 3A at the CheckThat2023
shared task. We make use of a generic approach that has been used to tackle a
diverse set of tasks, inspired by authorship attribution and profiling. We
train a number of Machine Learning models and our results show that Gradient
Boosting performs the best for both tasks. Based on the official ranking
provided by the shared task organizers, our model shows an average performance
compared to other teams.