Abstract
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 domainadaptive 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