  Entity Linking (EL) is a fundamental task for Information Extraction and
Knowledge Graphs. The general form of EL (i.e., end-to-end EL) aims to first
find mentions in the given input document and then link the mentions to
corresponding entities in a specific knowledge base. Recently, the paradigm of
retriever-reader promotes the progress of end-to-end EL, benefiting from the
advantages of dense entity retrieval and machine reading comprehension.
However, the existing study only trains the retriever and the reader separately
in a pipeline manner, which ignores the benefit that the interaction between
the retriever and the reader can bring to the task. To advance the
retriever-reader paradigm to perform more perfectly on end-to-end EL, we
propose BEER$^2$, a Bidirectional End-to-End training framework for Retriever
and Reader. Through our designed bidirectional end-to-end training, BEER$^2$
guides the retriever and the reader to learn from each other, make progress
together, and ultimately improve EL performance. Extensive experiments on
benchmarks of multiple domains demonstrate the effectiveness of our proposed
BEER$^2$.
