  Language usage changes over time, and this can impact the effectiveness of
NLP systems. This work investigates methods for adapting to changing discourse
during crisis events. We explore social media data during crisis, for which
effective, time-sensitive methods are necessary. We experiment with two
separate methods to accommodate changing data: temporal pretraining, which uses
unlabeled data for the target time periods to train better language models, and
a model of embedding shift based on tools for analyzing semantic change. This
shift allows us to counteract temporal drift by normalizing incoming data based
on observed patterns of language change. Simulating scenarios in which we lack
access to incoming labeled data, we demonstrate the effectiveness of these
methods for a wide variety of crises, showing we can improve performance by up
to 8.0 F1 score for relevance classification across datasets.
