KUCST at CheckThat 2023: How good can we be with a generic model?
This work addresses fact-checking tasks for the CheckThat community, but it is incremental as it applies an existing generic method without significant innovation.
The authors tackled tasks 2 and 3A in the CheckThat 2023 shared task using a generic machine learning approach, finding that Gradient Boosting performed best but achieved only average results compared to other teams.
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.