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

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.

5 pages; The AAAI-21 Workshop on Scientific Document Understanding

References in corpus (4)

• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
• Seeing Things from a Different Angle: Discovering Diverse Perspectives about Claims
• Scientific Claim Verification with VERT5ERINI
• A Richly Annotated Corpus for Different Tasks in Automated Fact-Checking

Cited by in corpus (3)

• Unsupervised Document Expansion for Information Retrieval with Stochastic Text Generation
• QMUL-SDS at SCIVER: Step-by-Step Binary Classification for Scientific Claim Verification
• Automated Fact-Checking: A Survey