Abstract
Incorporating terminology into a neural machine translation (NMT) system is a feature of interest for many users of machine translation. In this case study of English-French Canadian Parliamentary text, we examine the performance of standard NMT systems at handling terminology and consider the tradeoffs between potential performance improvements and the efforts required to maintain terminological resources specifically for NMT.- Anthology ID:
- 2023.eamt-1.47
- Volume:
- Proceedings of the 24th Annual Conference of the European Association for Machine Translation
- Month:
- June
- Year:
- 2023
- Address:
- Tampere, Finland
- Editors:
- Mary Nurminen, Judith Brenner, Maarit Koponen, Sirkku Latomaa, Mikhail Mikhailov, Frederike Schierl, Tharindu Ranasinghe, Eva Vanmassenhove, Sergi Alvarez Vidal, Nora Aranberri, Mara Nunziatini, Carla Parra Escartín, Mikel Forcada, Maja Popovic, Carolina Scarton, Helena Moniz
- Venue:
- EAMT
- SIG:
- Publisher:
- European Association for Machine Translation
- Note:
- Pages:
- 481–488
- Language:
- URL:
- https://aclanthology.org/2023.eamt-1.47
- DOI:
- Cite (ACL):
- Rebecca Knowles, Samuel Larkin, Marc Tessier, and Michel Simard. 2023. Terminology in Neural Machine Translation: A Case Study of the Canadian Hansard. In Proceedings of the 24th Annual Conference of the European Association for Machine Translation, pages 481–488, Tampere, Finland. European Association for Machine Translation.
- Cite (Informal):
- Terminology in Neural Machine Translation: A Case Study of the Canadian Hansard (Knowles et al., EAMT 2023)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2023.eamt-1.47.pdf