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During the onset of a disaster event, filtering relevant information from thesocial web data is challenging due to its sparse availability and practicallimitations in labeling datasets of an ongoing crisis. In this paper, wehypothesize that unsupervised domain adaptation through multi-task learning canbe a useful framework to leverage data from past crisis events for trainingefficient information filtering models during the sudden onset of a new crisis.We present a novel method to classify relevant tweets during an ongoing crisiswithout seeing any new examples, using the publicly available dataset of TRECincident streams. Specifically, we construct a customized multi-taskarchitecture with a multi-domain discriminator for crisis analytics: multi-taskdomain adversarial attention network. This model consists of dedicatedattention layers for each task to provide model interpretability; critical forreal-word applications. As deep networks struggle with sparse datasets, we showthat this can be improved by sharing a base layer for multi-task learning anddomain adversarial training. Evaluation of domain adaptation for crisis eventsis performed by choosing a target event as the test set and training on therest. Our results show that the multi-task model outperformed its single taskcounterpart. For the qualitative evaluation of interpretability, we show thatthe attention layer can be used as a guide to explain the model predictions andempower emergency services for exploring accountability of the model, byshowcasing the words in a tweet that are deemed important in the classificationprocess. Finally, we show a practical implication of our work by providing ause-case for the COVID-19 pandemic.

Unsupervised and Interpretable Domain Adaptation to Rapidly Filter Tweets for Emergency Services