  Zero-shot entity linking (EL) aims at aligning entity mentions to unseen
entities to challenge the generalization ability. Previous methods largely
focus on the candidate retrieval stage and ignore the essential candidate
ranking stage, which disambiguates among entities and makes the final linking
prediction. In this paper, we propose a read-and-select (ReS) framework by
modeling the main components of entity disambiguation, i.e., mention-entity
matching and cross-entity comparison. First, for each candidate, the reading
module leverages mention context to output mention-aware entity
representations, enabling mention-entity matching. Then, in the selecting
module, we frame the choice of candidates as a sequence labeling problem, and
all candidate representations are fused together to enable cross-entity
comparison. Our method achieves the state-of-the-art performance on the
established zero-shot EL dataset ZESHEL with a 2.55% micro-average accuracy
gain, with no need for laborious multi-phase pre-training used in most of the
previous work, showing the effectiveness of both mention-entity and
cross-entity interaction.
