A Paragraph-level Multi-task Learning Model for Scientific Fact-Verification
This work aims to provide an automatic fact-verification tool to combat the spread of scientific misinformation, which is a problem for both domain experts and the general public.
This paper addresses the challenge of automatically verifying scientific claims by proposing a paragraph-level multi-task learning model. The model jointly trains on rationale selection and stance prediction using contextualized sentence embeddings from BERT for the SciFact task.
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.