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

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 factverification tool becomes crucial for combating the spread of misinformation. In this work, we propose a novel, paragraphlevel, 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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