  Entity resolution (ER) is the task of identifying different representations
of the same real-world entities across databases. It is a key step for
knowledge base creation and text mining. Recent adaptation of deep learning
methods for ER mitigates the need for dataset-specific feature engineering by
constructing distributed representations of entity records. While these methods
achieve state-of-the-art performance over benchmark data, they require large
amounts of labeled data, which are typically unavailable in realistic ER
applications. In this paper, we develop a deep learning-based method that
targets low-resource settings for ER through a novel combination of transfer
learning and active learning. We design an architecture that allows us to learn
a transferable model from a high-resource setting to a low-resource one. To
further adapt to the target dataset, we incorporate active learning that
carefully selects a few informative examples to fine-tune the transferred
model. Empirical evaluation demonstrates that our method achieves comparable,
if not better, performance compared to state-of-the-art learning-based methods
while using an order of magnitude fewer labels.
