A Paragraph-level Multi-task Learning Model for Scientific Fact-Verification

• URL: http://arxiv.org/abs/2012.14500v2
• Date: Mon, 25 Jan 2021 02:29:18 GMT
• Title: A Paragraph-level Multi-task Learning Model for Scientific Fact-Verification
• Authors: Xiangci Li, Gully Burns, Nanyun Peng
• Abstract summary: It is a non-trivial task to verify a scientific claim by providing supporting or rationale evidences. In this work, we propose a 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.
• Score: 15.121389624346927
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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