Cross-Lingual and Cross-Domain Crisis Classification for Low-Resource Scenarios

TL;DR

This paper demonstrates that leveraging high-resource language data can effectively classify crisis-related social media messages across different languages and crisis types, addressing data scarcity in multilingual emergency response.

Contribution

It introduces a cross-lingual and cross-domain approach using a large unified dataset to improve crisis classification in low-resource languages and unseen crisis types.

Findings

Achieved 80.0% F1-score in cross-lingual crisis classification.

Demonstrated effective classification across different crisis domains.

Showed potential for real-time emergency response in low-resource settings.

Abstract

Social media data has emerged as a useful source of timely information about real-world crisis events. One of the main tasks related to the use of social media for disaster management is the automatic identification of crisis-related messages. Most of the studies on this topic have focused on the analysis of data for a particular type of event in a specific language. This limits the possibility of generalizing existing approaches because models cannot be directly applied to new types of events or other languages. In this work, we study the task of automatically classifying messages that are related to crisis events by leveraging cross-language and cross-domain labeled data. Our goal is to make use of labeled data from high-resource languages to classify messages from other (low-resource) languages and/or of new (previously unseen) types of crisis situations. For our study we consolidated from the literature a large unified dataset containing multiple crisis events and languages. Our empirical findings show that it is indeed possible to leverage data from crisis events in English to classify the same type of event in other languages, such as Spanish and Italian (80.0% F1-score). Furthermore, we achieve good performance for the cross-domain task (80.0% F1-score) in a cross-lingual setting. Overall, our work contributes to improving the data scarcity problem that is so important for multilingual crisis classification. In particular, mitigating cold-start situations in emergency events, when time is of essence.