Abstract
Artificial intelligence (AI) is quickly becoming an exciting new technology for the translation industry in form of large language models (LLMs). AI-based functionality could be used to improve the output of neural machine translation (NMT). One main issue that impacts MT quality and reliability is incorrect terminology. This is why STAR is making AI-powered terminology control a priority for its translation products because of the significant gains to be made - greatly improving the quality of MT output, reducing post editing (PE) costs and efforts, and thereby boosting overall translation productivity.- Anthology ID:
- 2024.eamt-2.13
- Volume:
- Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 2)
- Month:
- June
- Year:
- 2024
- Address:
- Sheffield, UK
- Editors:
- Carolina Scarton, Charlotte Prescott, Chris Bayliss, Chris Oakley, Joanna Wright, Stuart Wrigley, Xingyi Song, Edward Gow-Smith, Mikel Forcada, Helena Moniz
- Venue:
- EAMT
- SIG:
- Publisher:
- European Association for Machine Translation (EAMT)
- Note:
- Pages:
- 25–26
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2024.eamt-2.13/
- DOI:
- Cite (ACL):
- Marek Sabo, Judith Klein, and Giorgio Bernardinello. 2024. Boosting Machine Translation with AI-powered terminology features. In Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 2), pages 25–26, Sheffield, UK. European Association for Machine Translation (EAMT).
- Cite (Informal):
- Boosting Machine Translation with AI-powered terminology features (Sabo et al., EAMT 2024)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2024.eamt-2.13.pdf