Machine Translation (MT) is one of the oldest language technologies having been researched for more than 70 years. However, it is only during the last decade that it has been widely accepted by the general public, to the point where in many cases it has become an indispensable tool for the global community, supporting communication between nations and lowering language barriers. Still, there remain major gaps in the technology that need addressing before it can be successfully a0146pplied in under-resourced settings, can understand context and use world knowledge. This chapter provides an overview of the current state-of-the-art in the field of MT, offers technical and scientific forecasting for 2030, and provides recommendations for the advancement of MT as a critical technology if the goal of digital language equality in Europe is to be achieved.

Deep Dive Machine Translation / Skadiņa, Inguna; Vasiḷjevs, Andrejs; Pinnis, Mārcis; Bērziņš, Aivars; Aranberri, Nora; Van den Bogaert, Joachim; O’Connor, Sally; García-Martínez, Mercedes; Goenaga, Iakes; Hajič, Jan; Herranz, Manuel; Lieske, Christian; Popel, Martin; Popović, Maja; Castilho, Sheila; Gaspari, Federico; Rosa, Rudolf; Superbo, Riccardo; Way, Andy. - 280:(2023), pp. 263-287. [10.1007/978-3-031-28819-7_40]

Deep Dive Machine Translation

Federico Gaspari;
2023

Abstract

Machine Translation (MT) is one of the oldest language technologies having been researched for more than 70 years. However, it is only during the last decade that it has been widely accepted by the general public, to the point where in many cases it has become an indispensable tool for the global community, supporting communication between nations and lowering language barriers. Still, there remain major gaps in the technology that need addressing before it can be successfully a0146pplied in under-resourced settings, can understand context and use world knowledge. This chapter provides an overview of the current state-of-the-art in the field of MT, offers technical and scientific forecasting for 2030, and provides recommendations for the advancement of MT as a critical technology if the goal of digital language equality in Europe is to be achieved.
2023
978-3-031-28819-7
Deep Dive Machine Translation / Skadiņa, Inguna; Vasiḷjevs, Andrejs; Pinnis, Mārcis; Bērziņš, Aivars; Aranberri, Nora; Van den Bogaert, Joachim; O’Connor, Sally; García-Martínez, Mercedes; Goenaga, Iakes; Hajič, Jan; Herranz, Manuel; Lieske, Christian; Popel, Martin; Popović, Maja; Castilho, Sheila; Gaspari, Federico; Rosa, Rudolf; Superbo, Riccardo; Way, Andy. - 280:(2023), pp. 263-287. [10.1007/978-3-031-28819-7_40]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/925066
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