Farajian, M. Amin, Bertoldi, Nicola, Negri, Matteo, Turchi, Marco, Federico, Marcello
Evaluation of Terminology Translation in Instance-Based Neural MT Adaptation
Farajian, M. Amin, et al. “Evaluation of Terminology Translation in Instance-Based Neural MT Adaptation”. In: Pérez-Ortiz, Juan Antonio, et al. (Eds.). Proceedings of the 21st Annual Conference of the European Association for Machine Translation: 28-30 May 2018, Universitat d'Alacant, Alacant, Spain, pp. 149-158
URI: http://hdl.handle.net/10045/76037
DOI: 
ISSN: 
ISBN: 978-84-09-01901-4
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 corpus-based method.
Keywords:Machine Translation
European Association for Machine Translation
info:eu-repo/semantics/conferenceObject