MAGPIE：面向预训练表达识别的多任务媒体偏见分析泛化
MAGPIE: Multi-Task Media-Bias Analysis Generalization for Pre-Trained Identification of Expressions
AI总结：
针对传统媒体偏见检测单任务模型泛化性不足的问题，提出首个面向该任务的大规模多任务预训练方法MAGPIE及含59项任务的LBM数据集，在多个基准上取得更优表现且微调效率更高。
摘要：
媒体偏见检测是一个复杂的多维度问题，传统方法采用单任务模型和小型领域内数据集处理，因此缺乏泛化性。为解决这一问题，我们提出MAGPIE，这是首个专门针对媒体偏见检测的大规模多任务预训练方法。为实现大规模预训练，我们构建了Large Bias Mixture（LBM，大偏见混合集），包含59项偏见相关任务。在Bias Annotation By Experts（BABE，专家标注偏见）数据集上，MAGPIE的媒体偏见检测表现优于现有方法，F1分数相对提升3.3%。在Media Bias Identification Benchmark（MBIB，媒体偏见识别基准）的8项任务中，MAGPIE在5项上的表现优于此前模型。基于RoBERTa编码器，MAGPIE所需的微调步数仅为单任务方法的15%。例如，我们的评估显示，情感和情绪性等任务可促进整体学习，所有任务都能提升假新闻检测效果，且扩大任务规模可带来最优结果。MAGPIE证实多任务学习（MTL）是解决媒体偏见检测问题的有效途径，能够提升现有模型的准确性和效率。此外，LBM是首个可用的聚焦媒体偏见多任务学习的资源集合。
英文摘要：
Media bias detection poses a complex, multifaceted problem traditionally tackled using single-task models and small in-domain datasets, consequently lacking generalizability. To address this, we introduce MAGPIE, the first large-scale multi-task pre-training approach explicitly tailored for media bias detection. To enable pre-training at scale, we present Large Bias Mixture (LBM), a compilation of 59 bias-related tasks. MAGPIE outperforms previous approaches in media bias detection on the Bias Annotation By Experts (BABE) dataset, with a relative improvement of 3.3% F1-score. MAGPIE also performs better than previous models on 5 out of 8 tasks in the Media Bias Identification Benchmark (MBIB). Using a RoBERTa encoder, MAGPIE needs only 15% of finetuning steps compared to single-task approaches. Our evaluation shows, for instance, that tasks like sentiment and emotionality boost all learning, all tasks enhance fake news detection, and scaling tasks leads to the best results. MAGPIE confirms that MTL is a promising approach for addressing media bias detection, enhancing the accuracy and efficiency of existing models. Furthermore, LBM is the first available resource collection focused on media bias MTL.
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