Künstliche Intelligenz und Sprachtechnologien im Bereich der Translation

Abstract

Artificial Intelligence and Language Technologies in the Field of Translation
 
 The present paper deals with the opportunities which language technologies and artificial intelligence might offer for translation professionals. The paper also examines and elucidates that these technologies might contain possible problems, hurdles and obstacles. But the researcher reinforces that they also possess the necessary skills, competencies and prerequisites for the successful implementation and use of these technologies as illustrated in the Literature Review and Discussion parts of this research. It has also been shown that the successful implementation of such tools requires both institutional and technical prerequisites. A not inconsiderable problem, however, is the question of the what extent to which and whether the significance of the digital translator can also be adequately illustrated and clarified in the non-institutional private sector. Current developments in this respect unfortunately tend to point to the opposite, i.e. to the degradation of his role to a mere "helper" of the machine that (apparently) does most of the translation work. 
 Translators, then, will ultimately only be able to truly benefit from language technology and artificial intelligence if their status is not further compromised as a result. It is not very helpful if productivity and speed can be increased, but in inverse proportion to this, the fees constantly decrease, because it is made to appear that the computer is now doing most of the work. The end result of this development would be the digital slave rather than the highly professional digital language mediator. The paper projects that language technologies and artificial intelligence potentially offer benefits in the sense that they promote a better understanding and functioning of the symbiosis between humans and machines through the automation and streamlining of certain language-related workflow processes, i.e., a set of tasks that can be performed primarily - or even entirely - through workflow automation and with the help of content management software. Through the various past research on the subject and through secondary data analysis it has been analysed and stated in the paper that the potential acceleration of work processes improves overall efficiency by requiring less time to complete the same tasks. Therefore, this study evidently establishes the argument that artificial intelligence allows human actors to focus on more meaningful tasks associated with quality control, while routine and especially technical tasks are delegated (for the most part) to the machine. This also leaves more time for creative aspects. Artificial intelligence and language technology are not only relevant for translating, but also for interpreting, even if progress in this area is still somewhat slower and a paradigm shift has yet to occur. In this regard, conference interpreting is of particular interest. Problematically, the relevant software has so far lacked the cognitive, cultural, intellectual, and emotional skills that inevitably underlie qualitatively responsive interpretation. But at least it is possible to improve the interpreter's preparation for a meeting or conference. The relevant tools have so far only a supporting role to play. Language technologies and artificial intelligence potentially offer benefits in the sense that they promote a better understanding and functioning of the symbiosis between humans and machines through the automation and streamlining of certain language-related workflow processes, i.e., a set of tasks that can be performed mainly - or even entirely through workflow automation and with the help of content management software. Further development of artificial intelligence, machine translation has recently made significant progress. Nevertheless, it is common practice to first subject the machine-translated text to review or proofreading by a human translator. Post-editing is the process of using a machine-translated text as a basis and having it improved by a human translator. This means that the human translator ultimately creates the final translation.