  Evidence-based fact checking aims to verify the truthfulness of a claim
against evidence extracted from textual sources. Learning a representation that
effectively captures relations between a claim and evidence can be challenging.
Recent state-of-the-art approaches have developed increasingly sophisticated
models based on graph structures. We present a simple model that can be trained
on sequence structures. Our model enables inter-sentence attentions at
different levels and can benefit from joint training. Results on a large-scale
dataset for Fact Extraction and VERification (FEVER) show that our model
outperforms the graph-based approaches and yields 1.09% and 1.42% improvements
in label accuracy and FEVER score, respectively, over the best published model.
