Abstract:
The CheckThat! Lab is a challenging lab designed to address the issue of disinformation. We participated
in CheckThat! Lab Task 2, which is focused on classification of subjectivity in news articles. This shared
task included datasets in six different languages, as well as a multilingual dataset created by combining
all six languages. We followed standard preprocessing steps for Arabic, Dutch, English, German, Italian,
Turkish, and multilingual text data. We employed a transformer-based pretrained model, specifically
XLM-RoBERTa large, for our official submission to the CLEF Task 2. Our results were impressive, as
we achieved the 1st, 1st, 2nd, 5th, 2nd, 2nd, and 3rd positions on the leaderboard for the multilingual,
Arabic, Dutch, English, German, Italian, and Turkish text data, respectively. Furthermore, we also applied
BERT and BERT multilingual (BERT-m) models to assess the subjectivity of the text data. Our study
revealed that XLM-RoBERTa large outperformed BERT and BERT-m in all performance measures for
this particular dataset provided in the shared task.
