Conference Proceedings


Kepa Sarasola
Andy Way
Alberto Poncelas



translation resource machine translating translation quality language machine translation english mt evaluation

The ADAPT system description for the IWSLT 2018 Basque to English translation task (2018)

Abstract In this paper we present the ADAPT system built for the Basque to English Low Resource MT Evaluation Campaign. Basque is a low-resourced, morphologically-rich language. This poses a challenge for Neural Machine Translation models which usually achieve better performance when trained with large sets of data. Accordingly, we used synthetic data to improve the translation quality produced by a model built using only authentic data. Our proposal uses back-translated data to: (a) create new sentences, so the system can be trained with more data; and (b) translate sentences that are close to the test set, so the model can be fine-tuned to the document to be translated.
Collections Ireland -> Dublin City University -> Publication Type = Conference or Workshop Item
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Computing
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: ADAPT
Ireland -> Dublin City University -> Status = Published
Ireland -> Dublin City University -> Subject = Computer Science: Machine translating

Full list of authors on original publication

Kepa Sarasola, Andy Way, Alberto Poncelas

Experts in our system

Kepa Sarasola
Dublin City University
Total Publications: 3
Andy Way
Dublin City University
Total Publications: 229
Alberto Poncelas
Dublin City University
Total Publications: 8