  Election manifestos document the intentions, motives, and views of political
parties. They are often used for analysing a party's fine-grained position on a
particular issue, as well as for coarse-grained positioning of a party on the
left--right spectrum. In this paper we propose a two-stage model for
automatically performing both levels of analysis over manifestos. In the first
step we employ a hierarchical multi-task structured deep model to predict fine-
and coarse-grained positions, and in the second step we perform post-hoc
calibration of coarse-grained positions using probabilistic soft logic. We
empirically show that the proposed model outperforms state-of-art approaches at
both granularities using manifestos from twelve countries, written in ten
different languages.
