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

Autor:	Arana-Catania, M., Kochkina, Elena, Zubiaga, Arkaitz, Liakata, Maria, Procter, Rob, He, Yulan
Rok vydání:	2022
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence Computer Science - Computers and Society Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 16 pages, 1 figure, 8 tables, presented in NAACL 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2205.02596 Zobrazit plný text záznamu View this record from Arxiv