--- title: "Grammar Error Correction in Morphologically Rich Languages:" description: "This paper tests how well established English methods transfer to Russian, a highly inflected Slavic… 1 reference, cited by 51 papers here." authors: ["Alla Rozovskaya", "Dan Roth"] year: 2019 venue: "Transactions of the Association for Computational Linguistics" source: "https://aclanthology.org/Q19-1001/" doi: "10.1162/tacl_a_00251" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/rozovskaya-2019-grammar-error-correction-morphologically-rich/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/rozovskaya-2019-grammar-error-correction-morphologically-rich/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2019](https://md.research.elamur.ai/free/en/papers/2019/page/5/) / Grammar Error Correction in Morphologically Rich Languages: The Case of Russian # Grammar Error Correction in Morphologically Rich Languages: The Case of Russian **Authors:** [Alla Rozovskaya](https://md.research.elamur.ai/free/en/authors/alla-rozovskaya/) and [Dan Roth](https://md.research.elamur.ai/free/en/authors/dan-roth/) · **Year:** 2019 · **Venue:** [Transactions of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/tacl/) ([Transactions of the Association for Computational Linguistics (TACL) 2019](https://md.research.elamur.ai/free/en/venues/tacl/2019/)) · **Publisher:** [The MIT Press](https://md.research.elamur.ai/free/en/publishers/the-mit-press/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/Q19-1001/) · [PDF](https://aclanthology.org/Q19-1001.pdf) · [DOI 10.1162/tacl\_a\_00251](https://doi.org/10.1162/tacl_a_00251) · [OpenAlex](https://openalex.org/W2928406799) **Citations:** 75 citations (OpenAlex, 2026-10-14) · [51](https://md.research.elamur.ai/free/en/papers/rozovskaya-2019-grammar-error-correction-morphologically-rich/cited-by/) in our library, of the 141,840 papers whose bibliographies we read ## Summary Research on grammar error correction (GEC) has focused almost entirely on English, where systems rely on tens of millions of words of annotated learner text. This paper tests how well established English methods transfer to Russian, a highly inflected Slavic language with no comparable annotation. The authors corrected and error-tagged more than 200K words written by non-native authors, both foreign and heritage learners. They also designed an error taxonomy and released the data as a benchmark. On this corpus they compared two families of systems. The first is machine translation (MT), which depends on large parallel learner data. The second is error-specific classifiers trained with minimal supervision, which pair abundant native Russian text with a small annotated learner sample. All scores fall well below English levels, but the classifiers clearly outperform MT trained on the same data. The work matters for two reasons. It supplies a rare non-English GEC resource. It also shows that when annotation is scarce and words have many forms, combining cheap native data with modest annotation is the more practical approach. ## Key points - The new corpus covers more than 200K words of learner Russian, with corrections and error tags. It is distributed via the RULEC-GEC repository on GitHub and includes analysis of error rates for foreign and heritage learners. - Under the standard F0.5 metric, the minimal-supervision classifier system reaches 21.0, while the MT system trained on identical data reaches only 10.6. - Russian nouns and related words can take up to 24 inflected forms. This creates lexical sparsity, which large native corpora help offset. - Classifier precision is fairly good, but recall is low. Noun case and preposition errors reach roughly 25–36% recall, agreement errors about 16%, and verb aspect errors 9.1%. - Manual re-checking of false positives raised precision by 4–16 points for most error types. For prepositions, about 48% of flagged "false" corrections turned out to be acceptable alternatives. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* ## Institutions - [University of Pennsylvania](https://md.research.elamur.ai/free/en/universities/university-of-pennsylvania/) — University, Philadelphia, United States · 170 works · 60,739 citations Also on the byline: [Queens College, CUNY](https://ror.org/03v8adn41). 📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 9,085 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 8,888 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 9,296 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 9,206 works 🧭 Topics (2): [🧩 Morphology](https://md.research.elamur.ai/free/en/topics/morphology/) · 277 works · [🖍️ Grammatical error correction & learner language](https://md.research.elamur.ai/free/en/topics/grammatical-error-correction/) · 80 works ## Cite Cite as: Alla Rozovskaya and Dan Roth. 2019. Grammar Error Correction in Morphologically Rich Languages: The Case of Russian. Transactions of the Association for Computational Linguistics. The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @article{rozovskaya2019grammar, title = {{Grammar Error Correction in Morphologically Rich Languages: The Case of Russian}}, author = {Alla Rozovskaya and Dan Roth}, journal = {Transactions of the Association for Computational Linguistics}, year = {2019}, doi = {10.1162/tacl\_a\_00251}, url = {https://doi.org/10.1162/tacl_a_00251} } ``` - [← previous Interpretable Neural Predictions with Differentiable Binary Variables](https://md.research.elamur.ai/free/en/papers/bastings-2019-interpretable-neural-predictions-differentiable-binary/) - [next → Broad-Coverage Semantic Parsing as Transduction](https://md.research.elamur.ai/free/en/papers/zhang-2019-broad-coverage-semantic-parsing-as/) - [↑ back to papers of 2019](https://md.research.elamur.ai/free/en/papers/2019/page/5/) ## References 1 references, 1 of them with a card of ours. ↗ leads to the work. 1. Andrea Abel, Aivars Glaznieks, Lionel Nicolas, and Egol Stemle. 2014. [KoKo: An L1 learner corpus for German](https://md.research.elamur.ai/free/en/papers/abel-2014-koko-l1-learner-corpus-german/). In Proceedings of LREC, pages 2414–2421. *Updated 24 October 2026*