  Social media such as Twitter provide valuable information to crisis managers
and affected people during natural disasters. Machine learning can help
structure and extract information from the large volume of messages shared
during a crisis; however, the constantly evolving nature of crises makes
effective domain adaptation essential. Supervised classification is limited by
unchangeable class labels that may not be relevant to new events, and
unsupervised topic modelling by insufficient prior knowledge. In this paper, we
bridge the gap between the two and show that BERT embeddings finetuned on
crisis-related tweet classification can effectively be used to adapt to a new
crisis, discovering novel topics while preserving relevant classes from
supervised training, and leveraging bidirectional self-attention to extract
topic keywords. We create a dataset of tweets from a snowstorm to evaluate our
method's transferability to new crises, and find that it outperforms
traditional topic models in both automatic, and human evaluations grounded in
the needs of crisis managers. More broadly, our method can be used for textual
domain adaptation where the latent classes are unknown but overlap with known
classes from other domains.
