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

arXiv:2607.22376 · cs.CL · Submitted 2026-07-24 · Read on arXiv

cs.CL

Submitted: 2026-07-24

Updated: 2026-09-10

Comments: Accepted at CLiC-it 2026

License: http://creativecommons.org/licenses/by/4.0/

The gist: Most endangered languages lack the parallel data required for machine translation, despite the existence of descriptive grammar books.

Terminology

Fine-Tuning — 13× in this paper · explained in 5 episodes
Low-Resource Languages — 11× in this paper · explained in 4 episodes
Transformer — 6× in this paper · explained in 2 episodes
Data Augmentation — 5× in this paper · explained in 7 episodes
Synthetic Data Generation — 5× in this paper · explained in 5 episodes
Cosine Similarity — 4× in this paper · explained in 3 episodes
Embeddings — 4× in this paper · explained in 2 episodes
Empirical Methods in Natural Language Processing — 4× in this paper
Ablation — 3× in this paper · explained in 1 episode
Semantic Similarity — 3× in this paper · explained in 2 episodes
Ablation Studies — 2× in this paper · explained in 1 episode
Alignment — 2× in this paper · explained in 1 episode
Core — 2× in this paper · explained in 2 episodes
Analyzer — 1× in this paper · explained in 1 episode
Benchmark — 1× in this paper · explained in 2 episodes
Feature Extraction — 1× in this paper · explained in 2 episodes
Foundation Model — 1× in this paper · explained in 3 episodes
Foundation Models — 1× in this paper · explained in 7 episodes
Generalizability — 1× in this paper · explained in 6 episodes
Generative AI — 1× in this paper · explained in 2 episodes
Hallucination — 1× in this paper · explained in 3 episodes
In-Context Learning — 1× in this paper · explained in 4 episodes
In-Context Learning (ICL) — 1× in this paper · explained in 5 episodes
Structural Integrity — 1× in this paper · explained in 3 episodes

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 diverse low-resource languages-Kalamang (Papuan), Tuatschin (Romance), and Mandan (Siouan)-we show that fine-tuning on synthetic data improves over seed-data baselines in 75% of configurations for Kalamang and 59% for Tuatschin, with best-case ChrF++ gains of +8.8, +5.3, and +3.3 respectively. Through a systematic factorial study across 96 configurations varying target part-of-speech, retrieval granularity, and sample volume, we identify which factor combinations drive gains and where they break down. Our results demonstrate that static linguistic documentation can be repurposed for machine translation fine-tuning, offering a practical path towards translation tools for severely under-resourced languages.

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

Terminology

Abstract

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