Automatic Classification of Russian Learner Errors

LREC 2022  ·  Alla Rozovskaya ·

Grammatical Error Correction systems are typically evaluated overall, without taking into consideration performance on individual error types because system output is not annotated with respect to error type. We introduce a tool that automatically classifies errors in Russian learner texts. The tool takes an edit pair consisting of the original token(s) and the corresponding replacement and provides a grammatical error category. Manual evaluation of the output reveals that in more than 93% of cases the error categories are judged as correct or acceptable. We apply the tool to carry out a fine-grained evaluation on the performance of two error correction systems for Russian.

PDF Abstract


  Add Datasets introduced or used in this paper

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.


No methods listed for this paper. Add relevant methods here