论文标题
自然语言推断和自我注意力进行真实性评估大流行主张
Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims
论文作者
论文摘要
我们介绍了一项关于自动真实性评估的全面工作，从数据集创建到基于自然语言推断（NLI）的新方法，重点是与Covid-19-19的大流行有关的错误信息。我们首先描述了新颖的灵丹妙药数据集的构建，该数据集由Covid-19及其各自的信息来源组成。数据集构建包括有关检索技术和相似性测量的工作，以确保一套独特的索赔。然后，我们提出了基于自然语言推断的自动真实性评估的新技术，包括图形卷积网络和基于注意力的方法。我们使用拟议的技术对数据集进行了证据检索和准确评估进行了实验，并发现它们具有SOTA方法的竞争力，并提供了详细的讨论。
We present a comprehensive work on automated veracity assessment from dataset creation to developing novel methods based on Natural Language Inference (NLI), focusing on misinformation related to the COVID-19 pandemic. We first describe the construction of the novel PANACEA dataset consisting of heterogeneous claims on COVID-19 and their respective information sources. The dataset construction includes work on retrieval techniques and similarity measurements to ensure a unique set of claims. We then propose novel techniques for automated veracity assessment based on Natural Language Inference including graph convolutional networks and attention based approaches. We have carried out experiments on evidence retrieval and veracity assessment on the dataset using the proposed techniques and found them competitive with SOTA methods, and provided a detailed discussion.
