--- title: "Hi-GEC — paper by Sharma and Bhattacharyya (2025)" description: "Research on grammatical error correction (GEC) has concentrated on English and other well-resourced… 1 reference, cited by 9 papers here." authors: ["Ujjwal Sharma", "Pushpak Bhattacharyya"] year: 2025 venue: "Proceedings of the 31st International Conference on Computational Linguistics" source: "https://aclanthology.org/2025.coling-main.406/" 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/sharma-2025-hi-gec-hindi-grammar-error/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/sharma-2025-hi-gec-hindi-grammar-error/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2025](https://md.research.elamur.ai/free/en/papers/2025/page/9/) / Hi-GEC: Hindi Grammar Error Correction in Low Resource Scenario # Hi-GEC: Hindi Grammar Error Correction in Low Resource Scenario **Authors:** Ujjwal Sharma and [Pushpak Bhattacharyya](https://md.research.elamur.ai/free/en/authors/pushpak-bhattacharyya/) · **Year:** 2025 · **Venue:** [Proceedings of the 31st International Conference on Computational Linguistics](https://md.research.elamur.ai/free/en/venues/coling/) ([COLING 2025](https://md.research.elamur.ai/free/en/venues/coling/2025/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2025.coling-main.406/) · [PDF](https://aclanthology.org/2025.coling-main.406.pdf) **Citations:** 9 in our library, of the 149,439 papers whose bibliographies we read ## Summary Research on grammatical error correction (GEC) has concentrated on English and other well-resourced languages. Hindi, with its rich morphology and complex syntax, has had neither a manually annotated benchmark nor a systematic comparison of training-data strategies. The authors address both gaps. First, they mine the Hindi Wikipedia revision history with a modified Wikiedits tool, filter the edits heavily, label them with the ERRANT toolkit, and rebalance them into HiWikiEdits, a human-edited corpus covering 17 error categories. Second, they compare three sources of synthetic training pairs: - rule-based corruption of clean text (Direct-Noise); - Round Trip Translation (RTT) through several pivot languages using IndicTrans2; - a neural error generation model (EGM) trained to imitate the seed corpus. Pivoting through English, a distant but high-resource language, produced better training data than closely related Indic languages. Model-generated errors performed on par with the other methods. The best system was pretrained on combined synthetic data and then fine-tuned on HiWikiEdits, reaching 73.86 GLEU. The work gives Hindi its first GEC benchmark and reusable resources for other low-resource languages. ## Key points - **HiWikiEdits** contains 8,137 sentence pairs (5,696 train / 976 validation / 1,465 test) from a July 2024 Hindi Wikipedia dump. Edits were kept only if sentences had 10–30 words, the pair differed by at most 3 words, and the Levenshtein ratio was below 0.3. A NOOP class holds error-free sentences. - **Direct-Noise** provides about 300,000 pairs. Errors are injected at the word and character level following Grundkiewicz et al. (2019), using ASpell substitutions plus Hindi-specific matra (vowel-sign) replacements. - **RTT pivots** were English, Assamese, Marathi and Tamil. English gave a 6.25% GLEU improvement over Assamese and Marathi. BLEU-filtered RTT data (scores between 30 and 95) outperformed unfiltered data, suggesting data quality matters more than quantity. - **EGM-generated data** (about 100,000 pairs) improved GLEU by 17.54. This was not significantly different from the Direct-Noise-based model (M1). - **Release:** the best model (M3) scores 73.86 GLEU and handles adjective and pronoun inflection errors well. The dataset and scripts are on GitHub (ujjwalsharmaIITB/Hi-GEC), and the synthetic data is promised under CC-BY-SA 4.0. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* 📚 Shelves (2): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works 🧭 Topics (2): [🌱 Low-resource languages](https://md.research.elamur.ai/free/en/topics/low-resource-languages/) · 267 works · [🖍️ Grammatical error correction & learner language](https://md.research.elamur.ai/free/en/topics/grammatical-error-correction/) · 156 works ## Cite Cite as: Ujjwal Sharma and Pushpak Bhattacharyya. 2025. Hi-GEC: Hindi Grammar Error Correction in Low Resource Scenario. Proceedings of the 31st International Conference on Computational Linguistics. The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{sharma2025hi, title = {{Hi-GEC: Hindi Grammar Error Correction in Low Resource Scenario}}, author = {Ujjwal Sharma and Pushpak Bhattacharyya}, booktitle = {Proceedings of the 31st International Conference on Computational Linguistics}, year = {2025}, url = {https://aclanthology.org/2025.coling-main.406/} } ``` - [← previous Guiding Reasoning in Small Language Models with LLM Assistance](https://md.research.elamur.ai/free/en/papers/kim-2025-guiding-reasoning-small-language-models/) - [next → HS-STaR: Hierarchical Sampling for Self-Taught Reasoners via Difficulty Estimation and Budget Reallocation](https://md.research.elamur.ai/free/en/papers/xiong-2025-hs-star-hierarchical-sampling-self/) - [↑ back to papers of 2025](https://md.research.elamur.ai/free/en/papers/2025/page/9/) ## References 1 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Daniel Dahlmeier and Hwee Tou Ng. 2012. Better evaluation for grammatical error correction. In Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 568–572, Montréal, Canada. Association for Computationa… [find](https://scholar.google.com/scholar?q=Daniel+Dahlmeier+and+Hwee+Tou+Ng%2E+2012%2E+Better+evaluation+for+grammatical+error+correction%2E+In+Proceedings+of+the+2012+Conference+of+the+North+American+Chapter+of+the+Association+for+Computational+Lin) *Updated 30 October 2026*