Event-Related Bias Removal for Real-time Disaster Events

Salvador Medina Maza, Evangelia Spiliopoulou, Eduard Hovy, Alexander Hauptmann

Abstract
Social media has become an important tool to share information about crisis events such as natural disasters and mass attacks. Detecting actionable posts that contain useful information requires rapid analysis of huge volumes of data in real-time. This poses a complex problem due to the large amount of posts that do not contain any actionable information. Furthermore, the classification of information in real-time systems requires training on out-of-domain data, as we do not have any data from a new emerging crisis. Prior work focuses on models pre-trained on similar event types. However, those models capture unnecessary event-specific biases, like the location of the event, which affect the generalizability and performance of the classifiers on new unseen data from an emerging new event. In our work, we train an adversarial neural model to remove latent event-specific biases and improve the performance on tweet importance classification.
Anthology ID:
2020.findings-emnlp.344
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3858–3868
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.344
DOI:
10.18653/v1/2020.findings-emnlp.344
Bibkey:
Cite (ACL):
Salvador Medina Maza, Evangelia Spiliopoulou, Eduard Hovy, and Alexander Hauptmann. 2020. Event-Related Bias Removal for Real-time Disaster Events. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3858–3868, Online. Association for Computational Linguistics.
Cite (Informal):
Event-Related Bias Removal for Real-time Disaster Events (Medina Maza et al., Findings 2020)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2020.findings-emnlp.344.pdf
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