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Callaghan, Robert (2018) An exploration into the impact of neural machine translation in the service language domain. Masters thesis, Dublin, National College of Ireland.
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Abstract
While there is a large body of literature on machine translation there is very little known about the impact of the neural machine translation (NMT) model in the context of the service language domain. Through a inductive, exploratory research approach, using in-depth qualitative interviews with five machine translation experts from both academia and the business environment this smallscale research project aimed to address this gap in the literature.
The rich data garnered from the interviews highlighted the impact of disruption within the machine translation industry. The data also indicated how technological advances have allowed the machine translation industry to adapt and evolve rapidly. There has been a convergence of translation business models which has led to increased business benefits of the contemporary machine translation model. As the NMT model has gained traction within the Service Language Domain, the data gathered also underlined the importance of looking to the future of NMT models and how businesses can best benefit through their use.
Findings from this study provide up-to-date real world insight into NMT models and their impact in the service language domain. They also point to potential for further research in this area such how the NMT model measures quality output for certain language domains.
| Item Type: | Thesis (Masters) |
|---|---|
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science P Language and Literature > P Philology. Linguistics > Language Services |
| Divisions: | School of Business > Master of Business Administration |
| Depositing User: | Caoimhe Ní Mhaicín |
| Date Deposited: | 30 Oct 2018 10:57 |
| Last Modified: | 30 Oct 2018 10:57 |
| URI: | https://norma.ncirl.ie/id/eprint/3348 |
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