Whether the media faithfully communicate scientific information has long been
a core issue to the science community. Automatically identifying paraphrased
scientific findings could enable large-scale tracking and analysis of
information changes in the science communication process, but this requires
systems to understand the similarity between scientific information across
multiple domains. To this end, we present the SCIENTIFIC PARAPHRASE AND
INFORMATION CHANGE DATASET (SPICED), the first paraphrase dataset of scientific
findings annotated for degree of information change. SPICED contains 6,000
scientific finding pairs extracted from news stories, social media discussions,
and full texts of original papers. We demonstrate that SPICED poses a
challenging task and that models trained on SPICED improve downstream
performance on evidence retrieval for fact checking of real-world scientific
claims. Finally, we show that models trained on SPICED can reveal large-scale
trends in the degrees to which people and organizations faithfully communicate
new scientific findings. Data, code, and pre-trained models are available at
this http URL.

本文章介绍了 SCIENTIFIC PARAPHRASE AND INFORMATION CHANGE DATASET (SPICED) 数据集，用于科学信息改变的简述和识别。目的是为了了解人们如何忠实地传达科学发现，项目中设计的模型能够在证据检索方面提高性能并揭示外界是否忠实地传递新的科学发现。