在CheckThat上玩完!2023:通过基于风格的数据采样增强主观性检测

Ipek Baris Schlicht, Lynn Khellaf, Defne Altiok
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引用次数: 1

摘要

本文描述了我们在CheckThat!实验室。为了解决任务中的阶级不平衡,我们用GPT-3模型生成了额外的培训材料,使用了基于新闻视角的主观性检查表中不同风格的提示。我们使用扩展的训练集来微调特定于语言的转换器模型。我们对英语、德语和土耳其语的实验表明,不同的主观风格在所有语言中都是有效的。此外,我们观察到基于风格的过采样优于土耳其语和英语的释义。最后,GPT-3模型在生成非英语语言的基于风格的文本时,有时会产生平淡无奇的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DWReCO at CheckThat! 2023: Enhancing Subjectivity Detection through Style-based Data Sampling
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.
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