CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts

TL;DR

CrisisTransformers introduces specialized pre-trained language models and sentence encoders trained on crisis-related social media data, significantly improving NLP task performance for crisis informatics applications.

Contribution

The paper presents CrisisTransformers, a new ensemble of models trained on extensive crisis data, outperforming existing models in classification and sentence encoding tasks.

Findings

Models outperform baselines across all datasets.

Sentence encoder improves state-of-the-art by 17.43%.

Domain-specific models enhance semantic sentence embeddings.

Abstract

Social media platforms play an essential role in crisis communication, but analyzing crisis-related social media texts is challenging due to their informal nature. Transformer-based pre-trained models like BERT and RoBERTa have shown success in various NLP tasks, but they are not tailored for crisis-related texts. Furthermore, general-purpose sentence encoders are used to generate sentence embeddings, regardless of the textual complexities in crisis-related texts. Advances in applications like text classification, semantic search, and clustering contribute to the effective processing of crisis-related texts, which is essential for emergency responders to gain a comprehensive view of a crisis event, whether historical or real-time. To address these gaps in crisis informatics literature, this study introduces CrisisTransformers, an ensemble of pre-trained language models and sentence encoders trained on an extensive corpus of over 15 billion word tokens from tweets associated with more than 30 crisis events, including disease outbreaks, natural disasters, conflicts, and other critical incidents. We evaluate existing models and CrisisTransformers on 18 crisis-specific public datasets. Our pre-trained models outperform strong baselines across all datasets in classification tasks, and our best-performing sentence encoder improves the state-of-the-art by 17.43% in sentence encoding tasks. Additionally, we investigate the impact of model initialization on convergence and evaluate the significance of domain-specific models in generating semantically meaningful sentence embeddings. The models are publicly available at: https://huggingface.co/crisistransformers