  Given a natural language statement, how to verify whether it is supported,
refuted, or unknown according to a large-scale knowledge source like Wikipedia?
Existing neural-network-based methods often regard a sentence as a whole. While
we argue that it is beneficial to decompose a statement into multiple
verifiable logical points. In this paper, we propose LOREN, a novel approach
for fact verification that integrates both Logic guided Reasoning and Neural
inference. The key insight of LOREN is that it decomposes a statement into
multiple reasoning units around the central phrases. Instead of directly
validating a single reasoning unit, LOREN turns it into a question-answering
task and calculates the confidence of every single hypothesis using neural
networks in the embedding space. They are aggregated to make a final prediction
using a neural joint reasoner guided by a set of three-valued logic rules.
LOREN enjoys the additional merit of interpretability -- it is easy to explain
how it reaches certain results with intermediate results and why it makes
mistakes. We evaluate LOREN on FEVER, a public benchmark for fact verification.
Experiments show that our proposed LOREN outperforms other previously published
methods and achieves 73.43% of the FEVER score.
