  Blocking is a critical step in entity resolution, and the emergence of neural
network-based representation models has led to the development of dense
blocking as a promising approach for exploring deep semantics in blocking.
However, previous advanced self-supervised dense blocking approaches require
domain-specific training on the target domain, which limits the benefits and
rapid adaptation of these methods. To address this issue, we propose
UniBlocker, a dense blocker that is pre-trained on a domain-independent,
easily-obtainable tabular corpus using self-supervised contrastive learning. By
conducting domain-independent pre-training, UniBlocker can be adapted to
various downstream blocking scenarios without requiring domain-specific
fine-tuning. To evaluate the universality of our entity blocker, we also
construct a new benchmark covering a wide range of blocking tasks from multiple
domains and scenarios. Our experiments show that the proposed UniBlocker,
without any domain-specific learning, significantly outperforms previous self-
and unsupervised dense blocking methods and is comparable and complementary to
the state-of-the-art sparse blocking methods.
