--- title: "Automatic Generation of Graded Texts in Old Church Slavonic" description: "Graded readers grounded in comprehensible input have been developed for a few classical languages, notably Latin and… 8 references." authors: ["Iglika Nikolova-Stoupak", "Gaël Lejeune", "Aliona Shestakova-Stukun", "Eva Schaeffer-Lacroix"] year: 2026 venue: "Proceedings of the 2nd Workshop on Evaluating Text Difficulty in a Multilingual Context (DeTermIt! 2026)" source: "https://aclanthology.org/2026.determit-1.6/" doi: "10.63317/3mgy6qi4bkoq" licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)" licence_url: "https://creativecommons.org/licenses/by-nc/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/stoupak-2026-automatic-generation-graded-texts-old/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/stoupak-2026-automatic-generation-graded-texts-old/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/23/) / Automatic Generation of Graded Texts in Old Church Slavonic # Automatic Generation of Graded Texts in Old Church Slavonic **Authors:** [Iglika Nikolova-Stoupak](https://md.research.elamur.ai/free/en/authors/iglika-nikolova-stoupak/), [Gaël Lejeune](https://md.research.elamur.ai/free/en/authors/gael-lejeune/), Aliona Shestakova-Stukun and Eva Schaeffer-Lacroix · **Year:** 2026 · **Venue:** [Proceedings of the 2nd Workshop on Evaluating Text Difficulty in a Multilingual Context (DeTermIt! 2026)](https://md.research.elamur.ai/free/en/venues/determit/) ([Workshop on Evaluating Text Difficulty in a Multilingual Context 2026](https://md.research.elamur.ai/free/en/venues/determit/2026/)) · **Publisher:** [European Language Resources Association (ELRA)](https://md.research.elamur.ai/free/en/publishers/european-language-resources-association-elra/) · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2026.determit-1.6/) · [PDF](https://aclanthology.org/2026.determit-1.6.pdf) · [DOI 10.63317/3mgy6qi4bkoq](https://doi.org/10.63317/3mgy6qi4bkoq) · [OpenAlex](https://openalex.org/W7166504587) **Citations:** 0 citations (OpenAlex, 2026-10-14) ## Summary Graded readers grounded in comprehensible input have been developed for a few classical languages, notably Latin and Ancient Greek. No comparable material exists for Old Church Slavonic (OCS), a low-resourced language with considerable dialectal and diachronic variation. The authors explore whether such readers can be produced (semi-)automatically. First, they profile measurable textual features of six established readers in Latin, Greek and Biblical Hebrew to obtain level-specific reference values. Next, they adapt two OCS source texts: 'Genesis: 1' toward A1 and 'Saint George and the Dragon' toward B1. For this they use mT5, trained on multilingual professional graded readers, and GPT-5 with one-shot prompting. The outputs are judged by expert reading and by comparison with the reference profiles. mT5 failed at the task. GPT-5 produced pedagogically sensible text whose level differences follow the trends of professional readers. Its output still contained case errors, questionable word choices and interference from later Church Slavonic and Russian. The manually corrected texts are released, giving teachers a faster route to OCS reading material. ## Key points - The reference corpus consists of six readers: Lingua latina per se illustrata and Fabulae Faciles (Latin), Athenaze and Logos (Greek), and the Graded Reader of Biblical Hebrew and Biblical Hebrew Easy Stories (Hebrew). - The adapted texts are 'Genesis: 1' (A1 target, early-OCS reconstruction by Tomáš Spevák) and 'Saint George and the Dragon' (B1 target, later normalised OCS from Rystenko's monograph). - mT5 could not perform CEFR-guided adaptation, even when prompted in a higher-resourced language. GPT-5 in a one-shot setting yielded short sentences, simple grammar and dialogue. - The B1 output contained fewer verbs than the professional benchmark, giving it a "lack of action". A few A1 verses were too difficult despite the overall simplicity. - Manual post-editing took about one hour per text and remains the recommended step. The model's heavy reliance on input spelling conventions makes rule-based correction impractical. The edited GPT-5 texts are shared via GitHub. *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 - [Sorbonne Université](https://md.research.elamur.ai/free/en/universities/sorbonne-universite/) — University, Paris, France · 32 works · 7,270 citations - [Sens, Texte, Informatique, Histoire](https://md.research.elamur.ai/free/en/labs/sens-texte-informatique-histoire/) — Research institute, Paris, France · 4 works · 14 citations 📚 Shelves (4): [🖥️ 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 · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works 🧭 Topics (2): [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · 2,908 works · [🕎 Hebrew & Aramaic](https://md.research.elamur.ai/free/en/topics/hebrew-aramaic/) · 496 works ## Cite Cite as: Iglika Nikolova-Stoupak, Gaël Lejeune, Aliona Shestakova-Stukun and Eva Schaeffer-Lacroix. 2026. Automatic Generation of Graded Texts in Old Church Slavonic. Proceedings of the 2nd Workshop on Evaluating Text Difficulty in a Multilingual Context (DeTermIt! 2026). The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{nikolovastoupak2026automatic, title = {{Automatic Generation of Graded Texts in Old Church Slavonic}}, author = {Iglika Nikolova-Stoupak, Gaël Lejeune, Aliona Shestakova-Stukun and Eva Schaeffer-Lacroix}, booktitle = {Proceedings of the 2nd Workshop on Evaluating Text Difficulty in a Multilingual Context (DeTermIt! 2026)}, year = {2026}, doi = {10.63317/3mgy6qi4bkoq}, url = {https://doi.org/10.63317/3mgy6qi4bkoq} } ``` - [← previous Automatic Generation of Discharge Summaries Using Large Language Models: A Systematic Literature Review](https://md.research.elamur.ai/free/en/papers/pinar-2026-automatic-generation-discharge-summaries-large/) - [next → Automatic Lemmatisation for Norwegian](https://md.research.elamur.ai/free/en/papers/yildirim-2026-automatic-lemmatisation-norwegian/) - [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/23/) ## References 8 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. 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