  Existing natural language processing systems are vulnerable to noisy inputs
resulting from misspellings. On the contrary, humans can easily infer the
corresponding correct words from their misspellings and surrounding context.
Inspired by this, we address the stand-alone spelling correction problem, which
only corrects the spelling of each token without additional token insertion or
deletion, by utilizing both spelling information and global context
representations. We present a simple yet powerful solution that jointly detects
and corrects misspellings as a sequence labeling task by fine-turning a
pre-trained language model. Our solution outperforms the previous
state-of-the-art result by 12.8% absolute F0.5 score.
