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Public trust in science depends on honest and factual communication ofscientific papers. However, recent studies have demonstrated a tendency of newsmedia to misrepresent scientific papers by exaggerating their findings. Giventhis, we present a formalization of and study into the problem of exaggerationdetection in science communication. While there are an abundance of scientificpapers and popular media articles written about them, very rarely do thearticles include a direct link to the original paper, making data collectionchallenging. We address this by curating a set of labeled pressrelease/abstract pairs from existing expert annotated studies on exaggerationin press releases of scientific papers suitable for benchmarking theperformance of machine learning models on the task. Using limited data fromthis and previous studies on exaggeration detection in science, we introduceMT-PET, a multi-task version of Pattern Exploiting Training (PET), whichleverages knowledge from complementary cloze-style QA tasks to improve few-shotlearning. We demonstrate that MT-PET outperforms PET and supervised learningboth when data is limited, as well as when there is an abundance of data forthe main task.

Semi-Supervised Exaggeration Detection of Health Science Press Releases