  During the onset of a disaster event, filtering relevant information from the
social web data is challenging due to its sparse availability and practical
limitations in labeling datasets of an ongoing crisis. In this paper, we
hypothesize that unsupervised domain adaptation through multi-task learning can
be a useful framework to leverage data from past crisis events for training
efficient information filtering models during the sudden onset of a new crisis.
We present a novel method to classify relevant tweets during an ongoing crisis
without seeing any new examples, using the publicly available dataset of TREC
incident streams. Specifically, we construct a customized multi-task
architecture with a multi-domain discriminator for crisis analytics: multi-task
domain adversarial attention network. This model consists of dedicated
attention layers for each task to provide model interpretability; critical for
real-word applications. As deep networks struggle with sparse datasets, we show
that this can be improved by sharing a base layer for multi-task learning and
domain adversarial training. Evaluation of domain adaptation for crisis events
is performed by choosing a target event as the test set and training on the
rest. Our results show that the multi-task model outperformed its single task
counterpart. For the qualitative evaluation of interpretability, we show that
the attention layer can be used as a guide to explain the model predictions and
empower emergency services for exploring accountability of the model, by
showcasing the words in a tweet that are deemed important in the classification
process. Finally, we show a practical implication of our work by providing a
use-case for the COVID-19 pandemic.
