  Entity matching (EM) identifies data records that refer to the same
real-world entity. Despite the effort in the past years to improve the
performance in EM, the existing methods still require a huge amount of labeled
data in each domain during the training phase. These methods treat each domain
individually, and capture the specific signals for each dataset in EM, and this
leads to overfitting on just one dataset. The knowledge that is learned from
one dataset is not utilized to better understand the EM task in order to make
predictions on the unseen datasets with fewer labeled samples. In this paper,
we propose a new domain adaptation-based method that transfers the task
knowledge from multiple source domains to a target domain. Our method presents
a new setting for EM where the objective is to capture the task-specific
knowledge from pretraining our model using multiple source domains, then
testing our model on a target domain. We study the zero-shot learning case on
the target domain, and demonstrate that our method learns the EM task and
transfers knowledge to the target domain. We extensively study fine-tuning our
model on the target dataset from multiple domains, and demonstrate that our
model generalizes better than state-of-the-art methods in EM.
