A Knowledge Enhanced Learning and Semantic Composition Model for
  Multi-Claim Fact Checking
  To inhibit the spread of rumorous information and its severe consequences,
traditional fact checking aims at retrieving relevant evidence to verify the
veracity of a given claim. Fact checking methods typically use knowledge graphs
(KGs) as external repositories and develop reasoning mechanism to retrieve
evidence for verifying the triple claim. However, existing methods only focus
on verifying a single claim. As real-world rumorous information is more complex
and a textual statement is often composed of multiple clauses (i.e. represented
as multiple claims instead of a single one), multiclaim fact checking is not
only necessary but more important for practical applications. Although previous
methods for verifying a single triple can be applied repeatedly to verify
multiple triples one by one, they ignore the contextual information implied in
a multi-claim statement and could not learn the rich semantic information in
the statement as a whole. In this paper, we propose an end-to-end knowledge
enhanced learning and verification method for multi-claim fact checking. Our
method consists of two modules, KG-based learning enhancement and multi-claim
semantic composition. To fully utilize the contextual information, the KG-based
learning enhancement module learns the dynamic context-specific representations
via selectively aggregating relevant attributes of entities. To capture the
compositional semantics of multiple triples, the multi-claim semantic
composition module constructs the graph structure to model claim-level
interactions, and integrates global and salient local semantics with multi-head
attention. Experimental results on a real-world dataset and two benchmark
datasets show the effectiveness of our method for multi-claim fact checking
over KG.
