  Language use changes over time, and this impacts the effectiveness of NLP
systems. This phenomenon is even more prevalent in social media data during
crisis events where meaning and frequency of word usage may change over the
course of days. Contextual language models fail to adapt temporally,
emphasizing the need for temporal adaptation in models which need to be
deployed over an extended period of time. While existing approaches consider
data spanning large periods of time (from years to decades), shorter time spans
are critical for crisis data. We quantify temporal degradation for this
scenario and propose methods to cope with performance loss by leveraging
techniques from domain adaptation. To the best of our knowledge, this is the
first effort to explore effects of rapid language change driven by adversarial
adaptations, particularly during natural and human-induced disasters. Through
extensive experimentation on diverse crisis datasets, we analyze under what
conditions our approaches outperform strong baselines while highlighting the
current limitations of temporal adaptation methods in scenarios where access to
unlabeled data is scarce.
