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arXiv · 2211.11468

Enhancing Crisis-Related Tweet Classification with Entity-Masked Language Modeling and Multi-Task Learning

Philipp Seeberger·Korbinian Riedhammer
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Abstract

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.

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Philipp Seeberger, Korbinian Riedhammer. 2022-11-21. Enhancing Crisis-Related Tweet Classification with Entity-Masked Language Modeling and Multi-Task Learning. https://arxiv.org/abs/2211.11468

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