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.

我们使用GPT-3模型和基于新闻透视的主观性检查表的不同样式提示生成额外的训练材料，通过fine-tune语言特定的Transformer模型来解决任务中的类别不平衡问题，并证明不同主观风格在英语、德语和土耳其语的实验中都有效。此外，我们观察到在土耳其语和英语中，基于样式的过采样比释义更好。最后，当在非英语语言中生成基于样式的文本时，GPT-3模型有时会产生平庸的结果。

DWReCO在CheckThat! 2023中通过基于样式的数据采样增强主观性检测