Humanitarian disasters have been on the rise in recent years due to the
effects of climate change and socio-political situations such as the refugee
crisis. Technology can be used to best mobilize resources such as food and
water in the event of a natural disaster, by semi-automatically flagging tweets
and short messages as indicating an urgent need. The problem is challenging not
just because of the sparseness of data in the immediate aftermath of a
disaster, but because of the varying characteristics of disasters in developing
countries (making it difficult to train just one system) and the noise and
quirks in social media. In this paper, we present a robust, low-supervision
social media urgency system that adapts to arbitrary crises by leveraging both
labeled and unlabeled data in an ensemble setting. The system is also able to
adapt to new crises where an unlabeled background corpus may not be available
yet by utilizing a simple and effective transfer learning methodology.
Experimentally, our transfer learning and low-supervision approaches are found
to outperform viable baselines with high significance on myriad disaster
datasets.

本文旨在利用技术手段协助人道主义灾难时半自动识别推特和简短信息，以有效动员食物和水源等资源。作者提出了一个鲁棒性强的社交媒体紧急情况识别系统，并采用有标注和无标注数据集的集成方法，通过转移学习适应各种新危机。与现有基线模型相比，该系统的低监督和转移学习方法在数个灾难数据集上显著提高了预测准确度。