Towards a post-editing recommendation system for Spanish-Basque machine translation

Nora Aranberri, Jose A. Pascual


Abstract
The overall machine translation quality available for professional translators working with the Spanish–Basque pair is rather poor, which is a deterrent for its adoption. This work investigates the plausibility of building a comprehensive recommendation system to speed up decision time between post-editing or translation from scratch using the very limited training data available. First, we build a set of regression models that predict the post-editing effort in terms of overall quality, time and edits. Secondly, we build classification models that recommend the most efficient editing approach using post-editing effort features on top of linguistic features. Results show high correlations between the predictions of the regression models and the expected HTER, time and edit number values. Similarly, the results for the classifiers show that they are able to predict with high accuracy whether it is more efficient to translate or to post-edit a new segment.
Anthology ID:
2018.eamt-main.2
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:
41–50
Language:
URL:
https://aclanthology.org/2018.eamt-main.2
DOI:
Bibkey:
Cite (ACL):
Nora Aranberri and Jose A. Pascual. 2018. Towards a post-editing recommendation system for Spanish-Basque machine translation. In Proceedings of the 21st Annual Conference of the European Association for Machine Translation, pages 41–50, Alicante, Spain.
Cite (Informal):
Towards a post-editing recommendation system for Spanish-Basque machine translation (Aranberri & Pascual, EAMT 2018)
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PDF:
https://aclanthology.org/2018.eamt-main.2.pdf