LLM-based Machine Translation and Summarization for Latin
Martin Volk, Dominic Philipp Fischer, Lukas Fischer, Patricia Scheurer, Phillip Benjamin Ströbel
Abstract
This paper presents an evaluation of machine translation for Latin. We tested multilingual Large Language Models, in particular GPT-4, on letters from the 16th century that are in Latin and Early New High German. Our experiments include translation and cross-language summarization for the two historical languages into modern English and German. We show that LLM-based translation for Latin is clearly superior to previous approaches. We also show that LLM-based paraphrasing of Latin paragraphs from the historical letters produces English and German summaries that are close to human summaries published in the edition.- Anthology ID:
- 2024.lt4hala-1.15
- Volume:
- Proceedings of the Third Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) @ LREC-COLING-2024
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Rachele Sprugnoli, Marco Passarotti
- Venues:
- LT4HALA | WS
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 122–128
- Language:
- URL:
- https://aclanthology.org/2024.lt4hala-1.15
- DOI:
- Cite (ACL):
- Martin Volk, Dominic Philipp Fischer, Lukas Fischer, Patricia Scheurer, and Phillip Benjamin Ströbel. 2024. LLM-based Machine Translation and Summarization for Latin. In Proceedings of the Third Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA) @ LREC-COLING-2024, pages 122–128, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- LLM-based Machine Translation and Summarization for Latin (Volk et al., LT4HALA-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.lt4hala-1.15.pdf