Fine-Grained Language Relatedness for Zero-Shot Silesian-English Translation
| Authors | |
|---|---|
| Year of publication | 2023 |
| Type | Article in Proceedings |
| Conference | RASLAN 2023 Recent Advances in Slavonic Natural Language Processing |
| MU Faculty or unit | |
| Citation | |
| web | https://nlp.fi.muni.cz/raslan/raslan23.pdf#page=153 |
| Keywords | machine translation;large language models;English;Silesian;evaluation;zero-shot |
| Attached files | |
| Description | When parallel corpora are not available to train or fine-tune Machine Translation (MT) systems, one solution is to use data from a related language, and operate in a zero-shot setting. We explore the behaviour and performance of two pre-trained Large Language Models (LLMs) for zero-shot Silesian-English translation, by fine-tuning them on increasingly related languages. Our experiment shows that using data from related languages generally improves the zero-shot translation performance for our language pair, but the optimal fine-grained choice inside the Slavic language family is non-trivial and depends on the model characteristics. |
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