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Entity Linking (EL) is a fundamental task for Information Extraction andKnowledge Graphs. The general form of EL (i.e., end-to-end EL) aims to firstfind mentions in the given input document and then link the mentions tocorresponding entities in a specific knowledge base. Recently, the paradigm ofretriever-reader promotes the progress of end-to-end EL, benefiting from theadvantages of dense entity retrieval and machine reading comprehension.However, the existing study only trains the retriever and the reader separatelyin a pipeline manner, which ignores the benefit that the interaction betweenthe retriever and the reader can bring to the task. To advance theretriever-reader paradigm to perform more perfectly on end-to-end EL, wepropose BEER$^2$, a Bidirectional End-to-End training framework for Retrieverand Reader. Through our designed bidirectional end-to-end training, BEER$^2$guides the retriever and the reader to learn from each other, make progresstogether, and ultimately improve EL performance. Extensive experiments onbenchmarks of multiple domains demonstrate the effectiveness of our proposedBEER$^2$.

Bidirectional End-to-End Learning of Retriever-Reader Paradigm for Entity Linking