Rule-Based, Neural and LLM Back-Translation: Comparative Insights from a Variant of Ladin
Fuente:
arXiv
Guardado en:
| Autores principales: | , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866917720041193472 |
|---|---|
| author | Frontull, Samuel Moser, Georg |
| author_facet | Frontull, Samuel Moser, Georg |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_08819 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Rule-Based, Neural and LLM Back-Translation: Comparative Insights from a Variant of Ladin Frontull, Samuel Moser, Georg Computation and Language 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. |
| title | Rule-Based, Neural and LLM Back-Translation: Comparative Insights from a Variant of Ladin |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2407.08819 |