Work

Grammatical Error Correction via Sequence Tagging for Russian

Year: 2025

Type: conference-paper

Abstract: We introduce a modified sequence tagging architecture, proposed in (Omelianchuk et al., 2020), for the Grammatical Error Correction of the Russian language.We propose languagespecific operation set and preprocessing algorithm as well as a classification scheme which makes distinct predictions for insertions and other operations.The best versions of our models outperform previous approaches and set new SOTA on the two Russian GEC benchmarks -RU-Lang8 and GERA, while achieve competitive performance on RULEC-GEC.

Source: Annual Meeting of the Association for Computational Linguistics (ACL)

DOI: 10.18653/v1/2025.acl-srw.82

Authors: Regina Nasyrova, Alexey Sorokin

Institution: Lomonosov Moscow State University

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Lomonosov Moscow State University

OpenAlex ID: W4412889444