# A Language Model for Grammatical Error Correction in L2 Russian

arXiv: https://arxiv.org/abs/2307.01609 · HTML: https://mlsanity.com/paper/2307.01609/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 30 references that are in the map.
- First posted on arXiv 4 Jul 2023.

## Builds on (most cited first, 4 of 4)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Grammatical Error Correction in Low-Resource Scenarios](https://mlsanity.com/paper/1910.00353/) (2019)
- [Phrase-based Machine Translation is State-of-the-Art for Automatic Grammatical Error Correction](https://mlsanity.com/paper/1605.06353/) (2016)
- [The Unreasonable Effectiveness of Transformer Language Models in Grammatical Error Correction](https://mlsanity.com/paper/1906.01733/) (2019)

## Abstract (arXiv)

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.