  We present an overview of the CLEF-2018 CheckThat! Lab on Automatic
Identification and Verification of Political Claims, with focus on Task 1:
Check-Worthiness. The task asks to predict which claims in a political debate
should be prioritized for fact-checking. In particular, given a debate or a
political speech, the goal was to produce a ranked list of its sentences based
on their worthiness for fact checking. We offered the task in both English and
Arabic, based on debates from the 2016 US Presidential Campaign, as well as on
some speeches during and after the campaign. A total of 30 teams registered to
participate in the Lab and seven teams actually submitted systems for Task~1.
The most successful approaches used by the participants relied on recurrent and
multi-layer neural networks, as well as on combinations of distributional
representations, on matchings claims' vocabulary against lexicons, and on
measures of syntactic dependency. The best systems achieved mean average
precision of 0.18 and 0.15 on the English and on the Arabic test datasets,
respectively. This leaves large room for further improvement, and thus we
release all datasets and the scoring scripts, which should enable further
research in check-worthiness estimation.
