A Language Model for Grammatical Error Correction in L2 Russian
Nikita Remnev Sergei Obiedkov Ekaterina Rakhilina Ivan Smirnov Anastasia Vyrenkova
Grammatical error correction is one of the fundamental tasks in Natural
Language Processing. For the Russian language, most of the spellcheckers
available correct typos and other simple errors with high accuracy, but often
fail when faced with non-native (L2) writing, since the latter contains errors
that are not typical for native speakers. In this paper, we propose a pipeline
involving a language model intended for correcting errors in L2 Russian
writing. The language model proposed is trained on untagged texts of the
Newspaper subcorpus of the Russian National Corpus, and the quality of the
model is validated against the RULEC-GEC corpus.