KUCST at CheckThat 2023: How good can we be with a generic model?
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 18 references that are in the map. First posted on arXiv 15 Jun 2023.
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4 of 18 references.
References · 4
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2020We Can Detect Your Bias: Predicting the Political Ideology of News Articlescited by 92
- 2020Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularitycited by 32
- 2018Neural Based Statement Classification for Biased Languagecited by 17
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