synterr: rule-grounded synthetic error generation for Russian GEC
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| Format: | Recurso digital |
| Langue: | anglais |
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2026
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| _version_ | 1866901729570717696 |
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| author | Smirnova, Anna Kopan, Artyom Makeev, Vladislav Chernishev, George |
| author_facet | Smirnova, Anna Kopan, Artyom Makeev, Vladislav Chernishev, George |
| contents | <p>synterr is an open-source synthetic-error generator for Russian grammatical error correction. It corrupts clean text into realistic learner-like errors and labels each error with the prescriptive rule it violates, anchored in a 98-category taxonomy from Rozental's reference grammar.</p><p>Distinguishing properties: every error carries a defensible taxonomic label (Rozental §, RLC tag, or ERRANT tag) and a syntactic justification (dependency-tree-driven handlers for agreement, government, and punctuation). Output formats include GECToR token tags, parallel TSV, rich JSONL with rule labels, instruction-tuning chat format, and rule-targeted SFT.</p><p>This release accompanies the BEA 2026 paper <em>What Aggregate Scores Hide: Per-Rule Evaluation of Russian Grammatical Error Correction</em>.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20182862 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | synterr: rule-grounded synthetic error generation for Russian GEC Smirnova, Anna Kopan, Artyom Makeev, Vladislav Chernishev, George grammatical error correction GEC Russian synthetic data data augmentation natural language processing NLP Rozental rule-based generation dependency parsing <p>synterr is an open-source synthetic-error generator for Russian grammatical error correction. It corrupts clean text into realistic learner-like errors and labels each error with the prescriptive rule it violates, anchored in a 98-category taxonomy from Rozental's reference grammar.</p><p>Distinguishing properties: every error carries a defensible taxonomic label (Rozental §, RLC tag, or ERRANT tag) and a syntactic justification (dependency-tree-driven handlers for agreement, government, and punctuation). Output formats include GECToR token tags, parallel TSV, rich JSONL with rule labels, instruction-tuning chat format, and rule-targeted SFT.</p><p>This release accompanies the BEA 2026 paper <em>What Aggregate Scores Hide: Per-Rule Evaluation of Russian Grammatical Error Correction</em>.</p> |
| title | synterr: rule-grounded synthetic error generation for Russian GEC |
| topic | grammatical error correction GEC Russian synthetic data data augmentation natural language processing NLP Rozental rule-based generation dependency parsing |
| url | https://doi.org/10.5281/zenodo.20182862 |