How STAR Transit NXT can help translators measure and increase their MT post-editing efficiency

Julian Hamm, Judith Klein


Abstract
As machine translation (MT) is being more tightly integrated into modern CAT-based translation workflows, measuring and increasing MT efficiency has become one of the main concerns of LSPs and companies trying to optimise their processes in terms of quality and performance. When it comes to measur-ing MT efficiency, STAR’s CAT tool Transit NXT offers post-editing distance (PED) and MT error categorisation as two core features of Transit’s compre-hensive QA module. With DeepL glossa-ry integration and MT confidence scores, translators will also have access to two new features which can help them in-crease their MT post-editing efficiency.
Anthology ID:
2023.eamt-1.59
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:
513–514
Language:
URL:
https://aclanthology.org/2023.eamt-1.59
DOI:
Bibkey:
Cite (ACL):
Julian Hamm and Judith Klein. 2023. How STAR Transit NXT can help translators measure and increase their MT post-editing efficiency. In Proceedings of the 24th Annual Conference of the European Association for Machine Translation, pages 513–514, Tampere, Finland. European Association for Machine Translation.
Cite (Informal):
How STAR Transit NXT can help translators measure and increase their MT post-editing efficiency (Hamm & Klein, EAMT 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.eamt-1.59.pdf