EXPLAINABLE FACT-CHECKING THROUGH QUESTION ANSWERING

Autor(es)

Yang, Jing ; Vega-Oliveros, Didier ; Seibt, Tais ; Rocha, Anderson ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP); v. N/A, p. 5-pg., 2022-01-01.

Resumo

Misleading or false information has been creating chaos in some places around the world. To mitigate this issue, many researchers have proposed automated fact-checking methods to fight the spread of fake news. However, most methods cannot explain the reasoning behind their decisions, failing to build trust between machines and humans using such technology. Trust is essential for fact-checking to be applied in the real world. Here, we address fact-checking explainability through question answering. In particular, we propose generating questions and answers from claims and answering the same questions from evidence. We also propose an answer comparison model with an attention mechanism attached to each question. Leveraging question answering as a proxy, we break down automated fact-checking into several steps - this separation aids models' explainability as it allows for more detailed analysis of their decision-making processes. Experimental results show that the proposed model can achieve state-of-the-art performance while providing reasonable explainable capabilities(1). (AU)

• Processo FAPESP: 19/26283-5 - Aprendendo pistas visuais da passagem do tempo
• Beneficiário: Didier Augusto Vega Oliveros
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 17/12646-3 - Déjà vu: coerência temporal, espacial e de caracterização de dados heterogêneos para análise e interpretação de integridade
• Beneficiário: Anderson de Rezende Rocha
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 19/04053-8 - Reconstrução de eventos a partir de dados visuais heterogêneos
• Beneficiário: Jing Yang
• Modalidade de apoio: Bolsas no Brasil - Doutorado