  We extract a large-scale stance detection dataset from comments written by
candidates of elections in Switzerland. The dataset consists of German, French
and Italian text, allowing for a cross-lingual evaluation of stance detection.
It contains 67 000 comments on more than 150 political issues (targets). Unlike
stance detection models that have specific target issues, we use the dataset to
train a single model on all the issues. To make learning across targets
possible, we prepend to each instance a natural question that represents the
target (e.g. "Do you support X?"). Baseline results from multilingual BERT show
that zero-shot cross-lingual and cross-target transfer of stance detection is
moderately successful with this approach.
