Modeling Information Change in Science Communication with Semantically Matched Paraphrases
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning
Forlagets udgivne version, 649 KB, PDF-dokument
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/.
|Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
|Association for Computational Linguistics
|Udgivet - 2022
|2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022 - Abu Dhabi, United Arab Emirates
Varighed: 7 dec. 2022 → 11 dec. 2022
|2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022
|United Arab Emirates
|07/12/2022 → 11/12/2022
This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 801199 and a Rackham Graduate Student Research Grant at the University of Michigan.
© 2022 Association for Computational Linguistics.