A Paragraph-level Multi-task Learning Model for Scientific Fact-Verification
Xiangci Li, Gully Burns, and Nanyun Peng, in Scientific Document Understanding Workshop at the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), 2021.
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Abstract
Even for domain experts, it is a non-trivial task to verify a scientific claim by providing supporting or refuting evidence rationales. The situation worsens as misinformation is proliferated on social media or news websites, manually or programmatically, at every moment. As a result, an automatic fact-verification tool becomes crucial for combating the spread of misinformation. In this work, we propose a novel, paragraph-level, multi-task learning model for the SciFact task by directly computing a sequence of contextualized sentence embeddings from a BERT model and jointly training the model on rationale selection and stance prediction.
Bib Entry
@inproceedings{li2021paragraph,
title = {A Paragraph-level Multi-task Learning Model for Scientific Fact-Verification},
author = {Li, Xiangci and Burns, Gully and Peng, Nanyun},
booktitle = {Scientific Document Understanding Workshop at the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21)},
year = {2021}
}
Related Publications
A Paragraph-level Multi-task Learning Model for Scientific Fact-Verification
Xiangci Li, Gully Burns, and Nanyun Peng, in Scientific Document Understanding Workshop at the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), 2021.
Full Text Code Abstract BibTeX DetailsDetailsEven for domain experts, it is a non-trivial task to verify a scientific claim by providing supporting or refuting evidence rationales. The situation worsens as misinformation is proliferated on social media or news websites, manually or programmatically, at every moment. As a result, an automatic fact-verification tool becomes crucial for combating the spread of misinformation. In this work, we propose a novel, paragraph-level, multi-task learning model for the SciFact task by directly computing a sequence of contextualized sentence embeddings from a BERT model and jointly training the model on rationale selection and stance prediction.
@inproceedings{li2021paragraph, title = {A Paragraph-level Multi-task Learning Model for Scientific Fact-Verification}, author = {Li, Xiangci and Burns, Gully and Peng, Nanyun}, booktitle = {Scientific Document Understanding Workshop at the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21)}, year = {2021} }