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Zero-shot entity linking (EL) aims at aligning entity mentions to unseenentities to challenge the generalization ability. Previous methods largelyfocus on the candidate retrieval stage and ignore the essential candidateranking stage, which disambiguates among entities and makes the final linkingprediction. In this paper, we propose a read-and-select (ReS) framework bymodeling the main components of entity disambiguation, i.e., mention-entitymatching and cross-entity comparison. First, for each candidate, the readingmodule leverages mention context to output mention-aware entityrepresentations, enabling mention-entity matching. Then, in the selectingmodule, we frame the choice of candidates as a sequence labeling problem, andall candidate representations are fused together to enable cross-entitycomparison. Our method achieves the state-of-the-art performance on theestablished zero-shot EL dataset ZESHEL with a 2.55% micro-average accuracygain, with no need for laborious multi-phase pre-training used in most of theprevious work, showing the effectiveness of both mention-entity andcross-entity interaction.

A Read-and-Select Framework for Zero-shot Entity Linking