Democratizing Neural Machine Translation with OPUS-MT
This work addresses the problem of making neural machine translation more accessible and practical for end-users and developers across different platforms, though it appears incremental in nature.
The paper tackles the problem of limited language coverage and accessibility in machine translation by developing the OPUS ecosystem of open models and tools, resulting in increased translation quality and deployment on various platforms including regular desktops and small devices.
This paper presents the OPUS ecosystem with a focus on the development of open machine translation models and tools, and their integration into end-user applications, development platforms and professional workflows. We discuss our on-going mission of increasing language coverage and translation quality, and also describe on-going work on the development of modular translation models and speed-optimized compact solutions for real-time translation on regular desktops and small devices.