论文标题
利用基于变压器的多任务学习来检测新闻文章中的媒体偏见
Exploiting Transformer-based Multitask Learning for the Detection of Media Bias in News Articles
论文作者
论文摘要
媒体对公众对事件的看法有重大影响。关于任何主题的单方面或两极分化的观点通常被描述为媒体偏见。通过更改单词选择，可以引入新闻文章的偏见之一。有偏见的单词选择并不总是显而易见的，也不表现出很高的上下文依赖性。因此，发现偏见通常很困难。我们建议使用六个与偏见相关的数据集通过多任务学习训练的基于变压器的深度学习体系结构，以解决媒体偏见检测问题。我们表现最佳的实现实现了0.776的宏$ f_ {1} $，与我们的基线相比，性能提升为3 \％，表现优于现有方法。我们的结果表明，多任务学习是改善现有基线模型在识别倾斜报告时的一种有希望的替代方法。
Media has a substantial impact on the public perception of events. A one-sided or polarizing perspective on any topic is usually described as media bias. One of the ways how bias in news articles can be introduced is by altering word choice. Biased word choices are not always obvious, nor do they exhibit high context-dependency. Hence, detecting bias is often difficult. We propose a Transformer-based deep learning architecture trained via Multi-Task Learning using six bias-related data sets to tackle the media bias detection problem. Our best-performing implementation achieves a macro $F_{1}$ of 0.776, a performance boost of 3\% compared to our baseline, outperforming existing methods. Our results indicate Multi-Task Learning as a promising alternative to improve existing baseline models in identifying slanted reporting.
