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.

本研究提出了一种基于Hierarchical Character Tagger模型的短文本拼写错误纠正方法，该模型使用预训练的字符级语言模型作为文本编码器，并提出了一种分层多任务解码方法来缓解长尾标签分布问题。实验证明，HCTagger模型比许多现有模型更准确，速度更快。

短文本拼写错误修正的层次字符标记器