Rule-Based, Neural and LLM Back-Translation: Comparative Insights from a Variant of Ladin
<|reference_start|>Rule-Based, Neural and LLM Back-Translation: Comparative Insights from a Variant of Ladin: This paper explores the impact of different back-translation approaches on machine translation for Ladin, specifically the Val Badia variant. Given the limited amount of parallel data available for this language (only 18k Ladin-Italian sentence pairs), we investigate the performance of a multilingual neural machine translation model fine-tuned for Ladin-Italian. In addition to the available authentic data, we synthesise further translations by using three different models: a fine-tuned neural model, a rule-based system developed specifically for this language pair, and a large language model. Our experiments show that all approaches achieve comparable translation quality in this low-resource scenario, yet round-trip translations highlight differences in model performance.<|reference_end|>
@article{frontull2024rule-based,,
    title={Rule-Based, Neural and LLM Back-Translation: Comparative Insights from a
  Variant of Ladin},
    author={Samuel Frontull, Georg Moser},
    journal={arXiv preprint arXiv:2407.08819},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.08819},
    primaryClass={cs.CL}
}