  Address parsing consists of identifying the segments that make up an address,
such as a street name or a postal code. Because of its importance for tasks
like record linkage, address parsing has been approached with many techniques,
the latest relying on neural networks. While these models yield notable
results, previous work on neural networks has only focused on parsing addresses
from a single source country. This paper explores the possibility of
transferring the address parsing knowledge acquired by training deep learning
models on some countries' addresses to others with no further training in a
zero-shot transfer learning setting. We also experiment using an attention
mechanism and a domain adversarial training algorithm in the same zero-shot
transfer setting to improve performance. Both methods yield state-of-the-art
performance for most of the tested countries while giving good results to the
remaining countries. We also explore the effect of incomplete addresses on our
best model, and we evaluate the impact of using incomplete addresses during
training. In addition, we propose an open-source Python implementation of some
of our trained models.
