  Automatic assessment of the quality of arguments has been recognized as a
challenging task with significant implications for misinformation and targeted
speech. While real-world arguments are tightly anchored in context, existing
computational methods analyze their quality in isolation, which affects their
accuracy and generalizability. We propose SPARK: a novel method for scoring
argument quality based on contextualization via relevant knowledge. We devise
four augmentations that leverage large language models to provide feedback,
infer hidden assumptions, supply a similar-quality argument, or give a
counter-argument. SPARK uses a dual-encoder Transformer architecture to enable
the original argument and its augmentation to be considered jointly. Our
experiments in both in-domain and zero-shot setups show that SPARK consistently
outperforms existing techniques across multiple metrics.
