  User-generated information content has become an important information source
in crisis situations. However, classification models suffer from noise and
event-related biases which still poses a challenging task and requires
sophisticated task-adaptation. To address these challenges, we propose the use
of contrastive task-specialized sentence encoders for downstream
classification. We apply the task-specialization on the CrisisLex, HumAID, and
TrecIS information type classification tasks and show performance gains w.r.t.
F1-score. Furthermore, we analyse the cross-corpus and cross-lingual
capabilities for two German event relevancy classification datasets.
