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This same technology that enabled neural machine translation to improve machine translation is now finding its way into seemingly unrelated products (think ChatGPT or Dall·E). The translation “problem” has not been solved with the latest advancements in NMT, but there are lessons to be learned in other professions from how the professional translation community has been affected by and adapted to the widespread use of NMT both by translators and by end users.
Key principles, best practices, and real-world examples of how artificial intelligence and machine translation are being used to reduce costs and turn-around times, while...
By Dominique Bohbot Indigenous linguistic vitality is currently a large-scale undertaking in Canada. This article discusses the legal context, grammatical complexities, educational offerings, and...
With the democratization of AI, an engineer can build an MT model in one day. However, beating Google or DeepL in quality, performance, and...
