论文标题
科学事实验证的段落级多任务学习模型
A Paragraph-level Multi-task Learning Model for Scientific Fact-Verification
论文作者
论文摘要
即使对于领域专家，通过提供支持或反驳证据理由来验证科学主张是一项非凡的任务。由于错误信息在每时每刻都在社交媒体或新闻网站上激增，因此情况会恶化。结果，自动事实验证工具对于打击错误信息的传播至关重要。在这项工作中，我们通过直接从BERT模型中计算一系列上下文化的句子嵌入，并共同培训有关理由选择和立场预测的模型，为SCIFACT任务提出了一个新颖的段落级，多任务学习模型。
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.
