Modeling Information Change in Science Communication with Semantically Matched Paraphrases
This addresses the issue of monitoring how scientific information is faithfully communicated across media, though it is incremental as it builds on existing paraphrase and fact-checking tasks.
They tackled the problem of tracking information changes in science communication by creating SPICED, a dataset of 6,000 scientific finding pairs annotated for information change, which improves evidence retrieval for fact-checking and reveals trends in communication fidelity.
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 http://www.copenlu.com/publication/2022_emnlp_wright/.