  The widespread of fake news and misinformation in various domains ranging
from politics, economics to public health has posed an urgent need to
automatically fact-check information. A recent trend in fake news detection is
to utilize evidence from external sources. However, existing evidence-aware
fake news detection methods focused on either only word-level attention or
evidence-level attention, which may result in suboptimal performance. In this
paper, we propose a Hierarchical Multi-head Attentive Network to fact-check
textual claims. Our model jointly combines multi-head word-level attention and
multi-head document-level attention, which aid explanation in both word-level
and evidence-level. Experiments on two real-word datasets show that our model
outperforms seven state-of-the-art baselines. Improvements over baselines are
from 6\% to 18\%. Our source code and datasets are released at
\texttt{\url{https://github.com/nguyenvo09/EACL2021}}.
