  Given two large lists of records, the task in entity resolution (ER) is to
find the pairs from the Cartesian product of the lists that correspond to the
same real world entity. Typically, passive learning methods on tasks like ER
require large amounts of labeled data to yield useful models. Active Learning
is a promising approach for ER in low resource settings. However, the search
space, to find informative samples for the user to label, grows quadratically
for instance-pair tasks making active learning hard to scale. Previous works,
in this setting, rely on hand-crafted predicates, pre-trained language model
embeddings, or rule learning to prune away unlikely pairs from the Cartesian
product. This blocking step can miss out on important regions in the product
space leading to low recall. We propose DIAL, a scalable active learning
approach that jointly learns embeddings to maximize recall for blocking and
accuracy for matching blocked pairs. DIAL uses an Index-By-Committee framework,
where each committee member learns representations based on powerful
transformer models. We highlight surprising differences between the matcher and
the blocker in the creation of the training data and the objective used to
train their parameters. Experiments on five benchmark datasets and a
multilingual record matching dataset show the effectiveness of our approach in
terms of precision, recall and running time. Code is available at
https://github.com/ArjitJ/DIAL
