  State-of-the-art approaches to spelling error correction problem include
Transformer-based Seq2Seq models, which require large training sets and suffer
from slow inference time; and sequence labeling models based on Transformer
encoders like BERT, which involve token-level label space and therefore a large
pre-defined vocabulary dictionary. In this paper we present a Hierarchical
Character Tagger model, or HCTagger, for short text spelling error correction.
We use a pre-trained language model at the character level as a text encoder,
and then predict character-level edits to transform the original text into its
error-free form with a much smaller label space. For decoding, we propose a
hierarchical multi-task approach to alleviate the issue of long-tail label
distribution without introducing extra model parameters. Experiments on two
public misspelling correction datasets demonstrate that HCTagger is an accurate
and much faster approach than many existing models.
