Abstract
Machine Translation (MT) has now become an essential part of the localisation industry. New roles connected with it have emerged, and new technologies have been adopted.
As Language Service Providers (LSPs) need to implement these systems in their workflows – because of client demand, to improve cost efficiency, or to meet the rising demand for translated content – the need for clear guidelines to be followed in the adoption process grows.
In this paper, we describe in detail eight steps to integrate an MT workflow into the translation process. These steps have been identified by analysing existing literature and thoroughly validated through real-world MT implementations by STAR7.
The first step is to identify appropriate use cases. Then we must select the most suitable MT engine. Testing and benchmarking using scoring systems to evaluate the performance of the system is essential, as is supplier engagement in order to involve and inform all stakeholders. As the operational stage begins, several different Post-Editing Machine Translation (PEMT) workflows can be adopted. Quality Assurance (QA) and Language Quality Assessment (LQA) steps can then take place. Finally, feedback from all stakeholders can be collected to improve both the workflow and/or the MT engine performance itself.
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Poeta, N., Giai, E., Turnbull, D. (2022). Optimising the Machine Translation Workflow. In: Ceci, M., Flesca, S., Masciari, E., Manco, G., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2022. Lecture Notes in Computer Science(), vol 13515. Springer, Cham. https://doi.org/10.1007/978-3-031-16564-1_44
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DOI: https://doi.org/10.1007/978-3-031-16564-1_44
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