Machine translation for institutional academic texts: Output quality, terminology translation and post-editor trust

The present work is a feasibility study on the application of Machine Translation (MT) to institutional academic texts, specifically course catalogues, for Italian-English and German-English. The first research question of this work focuses on the feasibility of profitably applying MT to such texts. Since the benefits of a good quality MT might be counteracted by preconceptions of translators towards the output, the second research question examines translator trainees' trust towards an MT output as compared to a human translation (HT). Training and test sets are created for both language combinations in the institutional academic domain. MT systems used are ModernMT and Google Translate. Overall evaluations of the output quality are carried out using automatic metrics. Results show that applying neural MT to institutional academic texts can be beneficial even when bilingual data are not available. When small amounts of sentence pairs become available, MT quality improves. Then, a gold standard data set with manual annotations of terminology (MAGMATic) is created and used for an evaluation of the output focused on terminology translation. The gold standard was publicly released to stimulate research on terminology assessment. The assessment proves that domain-adaptation improves the quality of term translation. To conclude, a method to measure trust in a post-editing task is proposed and results regarding translator trainees trust towards MT are outlined. All participants are asked to work on the same text. Half of them is told that it is an MT output to be post-edited, and the other half that it is a HT needing revision. Results prove that there is no statistically significant difference between post-editing and HT revision in terms of number of edits and temporal effort. Results thus suggest that a new generation of translators that received training on MT and post-editing is not influenced by preconceptions against MT.