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

• URL: http://arxiv.org/abs/2211.11468v1
• Date: Mon, 21 Nov 2022 13:54:10 GMT
• Title: Enhancing Crisis-Related Tweet Classification with Entity-Masked Language Modeling and Multi-Task Learning
• Authors: Philipp Seeberger, Korbinian Riedhammer
• Abstract summary: 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.
• Score: 0.30458514384586394
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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