Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims

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.

Automated summary

This study focuses on automated veracity assessment methods, covering from dataset creation to the development of new technologies based on natural language inference, and concentrating on misinformation related to the COVID-19 pandemic. By constructing a novel dataset and proposing automated veracity assessment techniques based on natural language inference, these methods have shown competitive advantages in experiments compared to SOTA methods.