MultiVerS: Improving scientific claim verification with weak supervision and full-document context

David Wadden, Kyle Lo, Lucy Lu Wang, Arman Cohan, Iz Beltagy, Hannaneh Hajishirzi


Abstract
The scientific claim verification task requires an NLP system to label scientific documents which Support or Refute an input claim, and to select evidentiary sentences (or rationales) justifying each predicted label. In this work, we present MultiVerS, which predicts a fact-checking label and identifies rationales in a multitask fashion based on a shared encoding of the claim and full document context. This approach accomplishes two key modeling goals. First, it ensures that all relevant contextual information is incorporated into each labeling decision. Second, it enables the model to learn from instances annotated with a document-level fact-checking label, but lacking sentence-level rationales. This allows MultiVerS to perform weakly-supervised domain adaptation by training on scientific documents labeled using high-precision heuristics. Our approach outperforms two competitive baselines on three scientific claim verification datasets, with particularly strong performance in zero / few-shot domain adaptation experiments. Our code and data are available at https://github.com/dwadden/multivers.
Anthology ID:
2022.findings-naacl.6
Volume:
Findings of the Association for Computational Linguistics: NAACL 2022
Month:
July
Year:
2022
Address:
Seattle, United States
Editors:
Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
61–76
Language:
URL:
https://aclanthology.org/2022.findings-naacl.6
DOI:
10.18653/v1/2022.findings-naacl.6
Bibkey:
Cite (ACL):
David Wadden, Kyle Lo, Lucy Lu Wang, Arman Cohan, Iz Beltagy, and Hannaneh Hajishirzi. 2022. MultiVerS: Improving scientific claim verification with weak supervision and full-document context. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 61–76, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
MultiVerS: Improving scientific claim verification with weak supervision and full-document context (Wadden et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-naacl.6.pdf
Video:
 https://aclanthology.org/2022.findings-naacl.6.mp4
Code
 dwadden/longchecker +  additional community code
Data
CORD-19COVID-FactTREC-COVID