论文标题
Vert5erini的科学主张验证
Scientific Claim Verification with VERT5ERINI
论文作者
论文摘要
这项工作描述了预审预定的序列模型对生物医学领域中科学主张验证的任务的适应。我们提出了将T5利用抽象检索，句子选择和标签预测的Vert5erini，这是索赔验证的三个关键子任务。我们评估了我们对Scifact的管道，这是一个新策划的数据集，它要求模型不仅可以预测索赔的真实性，而且还提供了支持该决定的科学文献语料库的相关句子。从经验上讲，我们的管道在这三个步骤中的每个步骤中都优于强大的基线。最后，我们展示了Vert5erini使用不断扩展的Cord-19语料库中的证据概括为Covid-19的两个新数据集的能力。
This work describes the adaptation of a pretrained sequence-to-sequence model to the task of scientific claim verification in the biomedical domain. We propose VERT5ERINI that exploits T5 for abstract retrieval, sentence selection and label prediction, which are three critical sub-tasks of claim verification. We evaluate our pipeline on SCIFACT, a newly curated dataset that requires models to not just predict the veracity of claims but also provide relevant sentences from a corpus of scientific literature that support this decision. Empirically, our pipeline outperforms a strong baseline in each of the three steps. Finally, we show VERT5ERINI's ability to generalize to two new datasets of COVID-19 claims using evidence from the ever-expanding CORD-19 corpus.
