TY - CONF A1 - Seeberger, Philipp A1 - Riedhammer, Korbinian T1 - Enhancing Crisis-Related Tweet Classification with Entity-Masked Language Modeling and Multi-Task Learning N2 - Social media has become an important information source for crisis management and provides quick access to ongoing developments and critical information. However, classification models suffer from event-related biases and highly imbalanced label distributions which still poses a challenging task. To address these challenges, we propose a combination of entity-masked language modeling and hierarchical multi-label classification as a multi-task learning problem. We evaluate our method on tweets from the TREC-IS dataset and show an absolute performance gain w.r.t. F1-score of up to 10% for actionable information types. Moreover, we found that entity-masking reduces the effect of overfitting to in-domain events and enables improvements in cross-event generalization. Y1 - 2022 UR - https://opus4.kobv.de/opus4-ohm/frontdoor/index/index/docId/1789 SP - 70 EP - 78 PB - Association for Computational Linguistics ER -