Evaluation of Terminology Translation in Instance-Based Neural MT Adaptation
M. Amin Farajian, Nicola Bertoldi, Matteo Negri, Marco Turchi, Marcello Federico
Abstract
We address the issues arising when a neural machine translation engine trained on generic data receives requests from a new domain that contains many specific technical terms. Given training data of the new domain, we consider two alternative methods to adapt the generic system: corpus-based and instance-based adaptation. While the first approach is computationally more intensive in generating a domain-customized network, the latter operates more efficiently at translation time and can handle on-the-fly adaptation to multiple domains. Besides evaluating the generic and the adapted networks with conventional translation quality metrics, in this paper we focus on their ability to properly handle domain-specific terms. We show that instance-based adaptation, by fine-tuning the model on-the-fly, is capable to significantly boost the accuracy of translated terms, producing translations of quality comparable to the expensive corpusbased method.- Anthology ID:
- 2018.eamt-main.15
- Volume:
- Proceedings of the 21st Annual Conference of the European Association for Machine Translation
- Month:
- May
- Year:
- 2018
- Address:
- Alicante, Spain
- Editors:
- Juan Antonio Pérez-Ortiz, Felipe Sánchez-Martínez, Miquel Esplà-Gomis, Maja Popović, Celia Rico, André Martins, Joachim Van den Bogaert, Mikel L. Forcada
- Venue:
- EAMT
- SIG:
- Publisher:
- Note:
- Pages:
- 169–178
- Language:
- URL:
- https://aclanthology.org/2018.eamt-main.15
- DOI:
- Cite (ACL):
- M. Amin Farajian, Nicola Bertoldi, Matteo Negri, Marco Turchi, and Marcello Federico. 2018. Evaluation of Terminology Translation in Instance-Based Neural MT Adaptation. In Proceedings of the 21st Annual Conference of the European Association for Machine Translation, pages 169–178, Alicante, Spain.
- Cite (Informal):
- Evaluation of Terminology Translation in Instance-Based Neural MT Adaptation (Farajian et al., EAMT 2018)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2018.eamt-main.15.pdf