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

Miguel Arana-Catania, Elena Kochkina, Arkaitz Zubiaga, Maria Liakata, Rob Procter, Yulan He

NAACL. 2022.

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.

@inproceedings{arana2022natural,
  title={Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims},
  author={Arana-Catania, Miguel and Kochkina, Elena and Zubiaga, Arkaitz and Liakata, Maria and Procter, Robert and He, Yulan},
  booktitle={Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  pages={1496--1511},
  year={2022}
}