  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.
