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

Zero-Shot Entity Linking by Reading Entity Descriptions