  A fundamental challenge in the current NLP context, dominated by language
models, comes from the inflexibility of current architectures to 'learn' new
information. While model-centric solutions like continual learning or
parameter-efficient fine tuning are available, the question still remains of
how to reliably identify changes in language or in the world. In this paper, we
propose WikiTiDe, a dataset derived from pairs of timestamped definitions
extracted from Wikipedia. We argue that such resource can be helpful for
accelerating diachronic NLP, specifically, for training models able to scan
knowledge resources for core updates concerning a concept, an event, or a named
entity. Our proposed end-to-end method is fully automatic, and leverages a
bootstrapping algorithm for gradually creating a high-quality dataset. Our
results suggest that bootstrapping the seed version of WikiTiDe leads to better
fine-tuned models. We also leverage fine-tuned models in a number of downstream
tasks, showing promising results with respect to competitive baselines.
