This paper describes our submission for the subjectivity detection task at the CheckThat! Lab. To tackle class imbalances in the task, we have generated additional training materials with GPT-3 models using prompts of different styles from a subjectivity checklist based on journalistic perspective. We used the extended training set to fine-tune language-specific transformer models. Our experiments in English, German and Turkish demonstrate that different subjective styles are effective across all languages. In addition, we observe that the style-based oversampling is better than paraphrasing in Turkish and English. Lastly, the GPT-3 models sometimes produce lacklustre results when generating style-based texts in non-English languages.
@article{arxiv.2307.03550,
title = {DWReCO at CheckThat! 2023: Enhancing Subjectivity Detection through Style-based Data Sampling},
author = {Ipek Baris Schlicht and Lynn Khellaf and Defne Altiok},
journal= {arXiv preprint arXiv:2307.03550},
year = {2023}
}