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We present a comprehensive work on automated veracity assessment from datasetcreation to developing novel methods based on Natural Language Inference (NLI),focusing on misinformation related to the COVID-19 pandemic. We first describethe construction of the novel PANACEA dataset consisting of heterogeneousclaims on COVID-19 and their respective information sources. The datasetconstruction includes work on retrieval techniques and similarity measurementsto ensure a unique set of claims. We then propose novel techniques forautomated veracity assessment based on Natural Language Inference includinggraph convolutional networks and attention based approaches. We have carriedout experiments on evidence retrieval and veracity assessment on the datasetusing the proposed techniques and found them competitive with SOTA methods, andprovided a detailed discussion.

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