@inproceedings{seeberger-etal-2025-generalizing,
title = "Generalizing to Unseen Disaster Events: A Causal View",
author = "Seeberger, Philipp and
Freisinger, Steffen and
Bocklet, Tobias and
Riedhammer, Korbinian",
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-ijcnlp.2/",
doi = "10.18653/v1/2025.findings-ijcnlp.2",
pages = "28--37",
ISBN = "979-8-89176-303-6",
abstract = "Due to the rapid growth of social media platforms, these tools have become essential for monitoring information during ongoing disaster events. However, extracting valuable insights requires real-time processing of vast amounts of data. A major challenge in existing systems is their exposure to event-related biases, which negatively affects their ability to generalize to emerging events. While recent advancements in debiasing and causal learning offer promising solutions, they remain underexplored in the disaster event domain. In this work, we approach bias mitigation through a causal lens and propose a method to reduce event- and domain-related biases, enhancing generalization to future events. Our approach outperforms multiple baselines by up to +1.9{\%} F1 and significantly improves a PLM-based classifier across three disaster classification tasks."
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%0 Conference Proceedings
%T Generalizing to Unseen Disaster Events: A Causal View
%A Seeberger, Philipp
%A Freisinger, Steffen
%A Bocklet, Tobias
%A Riedhammer, Korbinian
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-303-6
%F seeberger-etal-2025-generalizing
%X Due to the rapid growth of social media platforms, these tools have become essential for monitoring information during ongoing disaster events. However, extracting valuable insights requires real-time processing of vast amounts of data. A major challenge in existing systems is their exposure to event-related biases, which negatively affects their ability to generalize to emerging events. While recent advancements in debiasing and causal learning offer promising solutions, they remain underexplored in the disaster event domain. In this work, we approach bias mitigation through a causal lens and propose a method to reduce event- and domain-related biases, enhancing generalization to future events. Our approach outperforms multiple baselines by up to +1.9% F1 and significantly improves a PLM-based classifier across three disaster classification tasks.
%R 10.18653/v1/2025.findings-ijcnlp.2
%U https://aclanthology.org/2025.findings-ijcnlp.2/
%U https://doi.org/10.18653/v1/2025.findings-ijcnlp.2
%P 28-37
Markdown (Informal)
[Generalizing to Unseen Disaster Events: A Causal View](https://aclanthology.org/2025.findings-ijcnlp.2/) (Seeberger et al., Findings 2025)
ACL
- Philipp Seeberger, Steffen Freisinger, Tobias Bocklet, and Korbinian Riedhammer. 2025. Generalizing to Unseen Disaster Events: A Causal View. In Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 28–37, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.