  We propose a character-based nonautoregressive GEC approach, with
automatically generated character transformations. Recently, per-word
classification of correction edits has proven an efficient, parallelizable
alternative to current encoder-decoder GEC systems. We show that word
replacement edits may be suboptimal and lead to explosion of rules for
spelling, diacritization and errors in morphologically rich languages, and
propose a method for generating character transformations from GEC corpus.
Finally, we train character transformation models for Czech, German and
Russian, reaching solid results and dramatic speedup compared to autoregressive
systems. The source code is released at
https://github.com/ufal/wnut2021_character_transformations_gec.
