  Most existing work on automated fact checking is concerned with predicting
the veracity of claims based on metadata, social network spread, language used
in claims, and, more recently, evidence supporting or denying claims. A crucial
piece of the puzzle that is still missing is to understand how to automate the
most elaborate part of the process -- generating justifications for verdicts on
claims. This paper provides the first study of how these explanations can be
generated automatically based on available claim context, and how this task can
be modelled jointly with veracity prediction. Our results indicate that
optimising both objectives at the same time, rather than training them
separately, improves the performance of a fact checking system. The results of
a manual evaluation further suggest that the informativeness, coverage and
overall quality of the generated explanations are also improved in the
multi-task model.
