FACE-KEG: Fact Checking Explained using KnowledgE Graphs

ABSTRACTIn recent years, a plethora of fact checking and fact verification techniques have been developed to detect the veracity or factuality of online information text for various applications. However, limited efforts have been undertaken to understand the interpretability of such veracity detection, i.e. explaining why a particular piece of text is factually correct or incorrect. In this work, we seek to bridge this gap by proposing a technique, FACE-KEG, to automatically perform explainable fact checking. Given an input fact or claim, our proposed model constructs a relevant knowledge graph for it from a large-scale structured knowledge base. This graph is encoded via a novel graph transforming encoder. Our model also simultaneously retrieves and encodes relevant textual context about the input text from the knowledge base. FACE-KEG then jointly exploits both the concept-relationship structure of the knowledge graph as well as semantic contextual cues in order to (i) detect the veracity of an input fact, and (ii) generate a human-comprehensible natural language explanation justifying the fact's veracity. We conduct extensive experiments on three large-scale datasets, and demonstrate the effectiveness of FACE-KEG while performing fact checking. Automatic and human evaluations further show that FACE-KEG significantly outperforms competitive baselines in learning concise, coherent and informative explanations for the input facts.