Abstract
This project aims to study the impact of adapting neural machine translation (NMT) systems through translation exemplars, determine the optimal similarity metric(s) for retrieving informative exemplars, and, verify the usefulness of this approach for domain adaptation of NMT systems.- Anthology ID:
- 2022.eamt-1.31
- Volume:
- Proceedings of the 23rd Annual Conference of the European Association for Machine Translation
- Month:
- June
- Year:
- 2022
- Address:
- Ghent, Belgium
- Editors:
- Helena Moniz, Lieve Macken, Andrew Rufener, Loïc Barrault, Marta R. Costa-jussà, Christophe Declercq, Maarit Koponen, Ellie Kemp, Spyridon Pilos, Mikel L. Forcada, Carolina Scarton, Joachim Van den Bogaert, Joke Daems, Arda Tezcan, Bram Vanroy, Margot Fonteyne
- Venue:
- EAMT
- SIG:
- Publisher:
- European Association for Machine Translation
- Note:
- Pages:
- 283–284
- Language:
- URL:
- https://aclanthology.org/2022.eamt-1.31
- DOI:
- Cite (ACL):
- Arda Tezcan. 2022. Dynamic Adaptation of Neural Machine-Translation Systems Through Translation Exemplars. In Proceedings of the 23rd Annual Conference of the European Association for Machine Translation, pages 283–284, Ghent, Belgium. European Association for Machine Translation.
- Cite (Informal):
- Dynamic Adaptation of Neural Machine-Translation Systems Through Translation Exemplars (Tezcan, EAMT 2022)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/2022.eamt-1.31.pdf