Tailoring Domain Adaptation for Machine Translation Quality Estimation - ACL Anthology

Tailoring Domain Adaptation for Machine Translation Quality Estimation

Javad Pourmostafa Roshan Sharami, Dimitar Shterionov, Frédéric Blain, Eva Vanmassenhove, Mirella De Sisto, Chris Emmery, Pieter Spronck


Abstract
While quality estimation (QE) can play an important role in the translation process, its effectiveness relies on the availability and quality of training data. For QE in particular, high-quality labeled data is often lacking due to the high-cost and effort associated with labeling such data. Aside from the data scarcity challenge, QE models should also be generalizabile, i.e., they should be able to handle data from different domains, both generic and specific. To alleviate these two main issues — data scarcity and domain mismatch — this paper combines domain adaptation and data augmentation within a robust QE system. Our method is to first train a generic QE model and then fine-tune it on a specific domain while retaining generic knowledge. Our results show a significant improvement for all the language pairs investigated, better cross-lingual inference, and a superior performance in zero-shot learning scenarios as compared to state-of-the-art baselines.
Anthology ID:
2023.eamt-1.2
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:
9–20
Language:
URL:
https://aclanthology.org/2023.eamt-1.2
DOI:
Bibkey:
Cite (ACL):
Javad Pourmostafa Roshan Sharami, Dimitar Shterionov, Frédéric Blain, Eva Vanmassenhove, Mirella De Sisto, Chris Emmery, and Pieter Spronck. 2023. Tailoring Domain Adaptation for Machine Translation Quality Estimation. In Proceedings of the 24th Annual Conference of the European Association for Machine Translation, pages 9–20, Tampere, Finland. European Association for Machine Translation.
Cite (Informal):
Tailoring Domain Adaptation for Machine Translation Quality Estimation (Sharami et al., EAMT 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.eamt-1.2.pdf