  Despite of the recent success of collective entity linking (EL) methods,
these "global" inference methods may yield sub-optimal results when the
"all-mention coherence" assumption breaks, and often suffer from high
computational cost at the inference stage, due to the complex search space. In
this paper, we propose a simple yet effective solution, called Dynamic Context
Augmentation (DCA), for collective EL, which requires only one pass through the
mentions in a document. DCA sequentially accumulates context information to
make efficient, collective inference, and can cope with different local EL
models as a plug-and-enhance module. We explore both supervised and
reinforcement learning strategies for learning the DCA model. Extensive
experiments show the effectiveness of our model with different learning
settings, base models, decision orders and attention mechanisms.
