  We present the zero-shot entity linking task, where mentions must be linked
to unseen entities without in-domain labeled data. The goal is to enable robust
transfer to highly specialized domains, and so no metadata or alias tables are
assumed. In this setting, entities are only identified by text descriptions,
and models must rely strictly on language understanding to resolve the new
entities. First, we show that strong reading comprehension models pre-trained
on large unlabeled data can be used to generalize to unseen entities. Second,
we propose a simple and effective adaptive pre-training strategy, which we term
domain-adaptive pre-training (DAP), to address the domain shift problem
associated with linking unseen entities in a new domain. We present experiments
on a new dataset that we construct for this task and show that DAP improves
over strong pre-training baselines, including BERT. The data and code are
available at https://github.com/lajanugen/zeshel.
