Abstract
The mission of the Directorate General for Translation (DGT) is to provide high-quality translation to help the European Commission communicate with EU citizens. To this end DGT employs almost 2000 translators from all EU official languages. But while the demand for translation has been continuously growing, following a global trend, the number of translators has decreased. To cope with the demand, DGT extensively uses a CAT environment encompassing translation memories, terminology databases and recently also machine translation. This paper examines the benefits and risks of using neural machine translation to augment the productivity of in‒house DGT translators for the English‒Polish language pair. Based on the analysis of a sample of NMT‒translated texts and on the observations of the working practices of Polish translators it is concluded that the possible productivity gain is still modest, while the risks to quality are quite substantial.- Anthology ID:
- 2020.eamt-1.28
- Volume:
- Proceedings of the 22nd Annual Conference of the European Association for Machine Translation
- Month:
- November
- Year:
- 2020
- Address:
- Lisboa, Portugal
- Editors:
- André Martins, Helena Moniz, Sara Fumega, Bruno Martins, Fernando Batista, Luisa Coheur, Carla Parra, Isabel Trancoso, Marco Turchi, Arianna Bisazza, Joss Moorkens, Ana Guerberof, Mary Nurminen, Lena Marg, Mikel L. Forcada
- Venue:
- EAMT
- SIG:
- Publisher:
- European Association for Machine Translation
- Note:
- Pages:
- 263–269
- Language:
- URL:
- https://aclanthology.org/2020.eamt-1.28
- DOI:
- Cite (ACL):
- Karolina Stefaniak. 2020. Evaluating the usefulness of neural machine translation for the Polish translators in the European Commission. In Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, pages 263–269, Lisboa, Portugal. European Association for Machine Translation.
- Cite (Informal):
- Evaluating the usefulness of neural machine translation for the Polish translators in the European Commission (Stefaniak, EAMT 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.eamt-1.28.pdf