A Factorial Study of Synthetic Data Generation for Low-Resource Machine Translation using Grammar Books

Excerpt

arXiv:2607.22376v2 Announce Type: replace Abstract: Most endangered languages lack the parallel data required for machine translation, despite the existence of descriptive grammar books. We introduce a pipeline that uses large language models to extract grammatical rules, example sentences, and lexicons from grammar books and generate synthetic parallel corpora for fine-tuning-rather than feeding grammar content into prompts at inference time, as in prior work. Validated on three typologically d