  We propose an autoregressive entity linking model, that is trained with two
auxiliary tasks, and learns to re-rank generated samples at inference time. Our
proposed novelties address two weaknesses in the literature. First, a recent
method proposes to learn mention detection and then entity candidate selection,
but relies on predefined sets of candidates. We use encoder-decoder
autoregressive entity linking in order to bypass this need, and propose to
train mention detection as an auxiliary task instead. Second, previous work
suggests that re-ranking could help correct prediction errors. We add a new,
auxiliary task, match prediction, to learn re-ranking. Without the use of a
knowledge base or candidate sets, our model sets a new state of the art in two
benchmark datasets of entity linking: COMETA in the biomedical domain, and
AIDA-CoNLL in the news domain. We show through ablation studies that each of
the two auxiliary tasks increases performance, and that re-ranking is an
important factor to the increase. Finally, our low-resource experimental
results suggest that performance on the main task benefits from the knowledge
learned by the auxiliary tasks, and not just from the additional training data.
