  We study cross-lingual stance detection, which aims to leverage labeled data
in one language to identify the relative perspective (or stance) of a given
document with respect to a claim in a different target language. In particular,
we introduce a novel contrastive language adaptation approach applied to memory
networks, which ensures accurate alignment of stances in the source and target
languages, and can effectively deal with the challenge of limited labeled data
in the target language. The evaluation results on public benchmark datasets and
comparison against current state-of-the-art approaches demonstrate the
effectiveness of our approach.
