Explainable Fact-checking through Question Answering
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingFact CheckingQuestion AnsweringSimilar Papers 제목 키워드 기반
QACHECK: A Demonstration System for Question-Guided Multi-Hop Fact-Checking
Fact-checking real-world claims often requires complex, multi-step reasoning due to the absence of direct evidence to support or refute them. However, existing fact-checking systems often lack transparency in their decis…
Decision MakingFact CheckingQuestion AnsweringAnswerFact: Fact Checking in Product Question Answering
Product-related question answering platforms nowadays are widely employed in many E-commerce sites, providing a convenient way for potential customers to address their concerns during online shopping. However, the misinf…
Fact CheckingMisinformationQuestion AnsweringFACTIFY-5WQA: 5W Aspect-based Fact Verification through Question Answering
Automatic fact verification has received significant attention recently. Contemporary automatic fact-checking systems focus on estimating truthfulness using numerical scores which are not human-interpretable. A human fac…
Fact CheckingFact VerificationLanguage ModellingQuestion Answering+1A Fact Checking and Verification System for FEVEROUS Using a Zero-Shot Learning Approach
In this paper, we propose a novel fact checking and verification system to check claims against Wikipedia content. Our system retrieves relevant Wikipedia pages using Anserini, uses BERT-large-cased question answering mo…
Fact CheckingNatural Language InferenceQuestion AnsweringZero-Shot LearningSemEval-2019 Task 8: Fact Checking in Community Question Answering Forums
We present SemEval-2019 Task 8 on Fact Checking in Community Question Answering Forums, which features two subtasks. Subtask A is about deciding whether a question asks for factual information vs. an opinion/advice vs. j…
Community Question AnsweringFact CheckingQuestion Answering