Modeling Information Change in Science Communication with Semantically Matched Paraphrases
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Modeling Information Change in Science Communication with Semantically Matched Paraphrases. / Wright, Dustin; Pei, Jiaxin; Jurgens, David; Augenstein, Isabelle.
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2022. p. 1783-1807.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research
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TY - GEN
T1 - Modeling Information Change in Science Communication with Semantically Matched Paraphrases
AU - Wright, Dustin
AU - Pei, Jiaxin
AU - Jurgens, David
AU - Augenstein, Isabelle
N1 - Publisher Copyright: © 2022 Association for Computational Linguistics.
PY - 2022
Y1 - 2022
N2 - 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/.
AB - 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/.
UR - http://www.scopus.com/inward/record.url?scp=85149438687&partnerID=8YFLogxK
M3 - Article in proceedings
AN - SCOPUS:85149438687
SP - 1783
EP - 1807
BT - Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
PB - Association for Computational Linguistics
T2 - 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022
Y2 - 7 December 2022 through 11 December 2022
ER -
ID: 341062805