  This paper investigates how to correct Chinese text errors with types of
mistaken, missing and redundant characters, which is common for Chinese native
speakers. Most existing models based on detect-correct framework can correct
mistaken characters errors, but they cannot deal with missing or redundant
characters. The reason is that lengths of sentences before and after correction
are not the same, leading to the inconsistence between model inputs and
outputs. Although the Seq2Seq-based or sequence tagging methods provide
solutions to the problem and achieved relatively good results on English
context, but they do not perform well in Chinese context according to our
experimental results. In our work, we propose a novel detect-correct framework
which is alignment-agnostic, meaning that it can handle both text aligned and
non-aligned occasions, and it can also serve as a cold start model when there
are no annotated data provided. Experimental results on three datasets
demonstrate that our method is effective and achieves the best performance
among existing published models.
