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arXiv · 2312.11020

Information Type Classification with Contrastive Task-Specialized Sentence Encoders

Philipp Seeberger·Tobias Bocklet·Korbinian Riedhammer
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Abstract

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.

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Philipp Seeberger, Tobias Bocklet, Korbinian Riedhammer. 2023-12-18. Information Type Classification with Contrastive Task-Specialized Sentence Encoders. https://arxiv.org/abs/2312.11020

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