Reasoning Over Semantic-Level Graph for Fact Checking

We study fact-checking in this paper, which aims to verify a textual claim given textual evidence (e.g., retrieved sentences from Wikipedia). Existing studies typically either concatenate retrieved sentences as a single string or use feature fusion on the top of features of sentences, while ignoring semantic-level information including participants, location, and temporality of an event occurred in a sentence and relationships among multiple events. Such semantic-level information is crucial for understanding the relational structure of evidence and the deep reasoning procedure over that. In this paper, we address this issue by proposing a graph-based reasoning framework, called the Dynamic REAsoning Machine (DREAM) framework. We first construct a semantic-level graph, where nodes are extracted by semantic role labeling toolkits and are connected by inner- and inter- sentence edges. After having the automatically constructed graph, we use XLNet as the backbone of our approach and propose a graph-based contextual word representation learning module and a graph-based reasoning module to leverage the information of graphs. The first module is designed by considering a claim as a sequence, in which case we use the graph structure to re-define the relative distance of words. On top of this, we propose the second module by considering both the claim and the evidence as graphs and use a graph neural network to capture the semantic relationship at a more abstract level. We conduct experiments on FEVER, a large-scale benchmark dataset for fact-checking. Results show that both of the graph-based modules improve performance. Our system is the state-of-the-art system on the public leaderboard in terms of both accuracy and FEVER score.