Grammatical Error Correction via Sequence Tagging for Russian

Regina Nasyrova, Alexey Sorokin

Keywords: Grammatical Error Correction, Sequence tagging, GEC, NLP

TL;DR: Sequence Tagging architecture for Russian outperforms other approaches on RU-Lang8 and GERA.

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 language-specific 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.

Archival Status: Archival

Paper Length: Long Paper (up to 8 pages of content)

Submission Number: 320