  Question Answering systems are generally modelled as a pipeline consisting of
a sequence of steps. In such a pipeline, Entity Linking (EL) is often the first
step. Several EL models first perform span detection and then entity
disambiguation. In such models errors from the span detection phase cascade to
later steps and result in a drop of overall accuracy. Moreover, lack of gold
entity spans in training data is a limiting factor for span detector training.
Hence the movement towards end-to-end EL models began where no separate span
detection step is involved. In this work we present a novel approach to
end-to-end EL by applying the popular Pointer Network model, which achieves
competitive performance. We demonstrate this in our evaluation over three
datasets on the Wikidata Knowledge Graph.
