The OPTIMICE Project: Optimising Translation Quality of Metadata in the Editorial Chain of Academic Journals

Abstract

The OPTIMICE project has developed a method that combines neural machine translation and human post-editing to enhance the quality of article metadata when translating from French to English as part of the journal editorial process. In partnership with the LIDILE research unit, the PUR (a French publisher) and the MSHB (French Centre for Human Sciences), we comparatively assessed the quality of human and machine translations of the metadata of 32 articles using our proprietary quality assessment grid and professional translators. The aim was to precisely determine the qualitative elements and limitations of each output, and to design the most appropriate translation method. We then formulated recommendations for writing and translating metadata to complement guidelines for authors, and improve the acceptance, referencing and international visibility of papers in journals. The method was finally tested on 2021 issues of the 4 selected journals, focusing on history, archaeology, education and geography respectively. The objective is to develop a methodology that can be reproduced and transferred to other journals, languages and disciplinary fields.