MAGPIE: Multi-Task Media-Bias Analysis Generalization for Pre-Trained Identification of Expressions
arxiv(2024)
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
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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