[Translation Cafe Summary] What is 'Context' that determines the quality of AI translation?
Overview
In the 12th session of Translation Cafe Season 9, 'Multilingual Communication Technology,' we covered how to incorporate translation memory (TM), terminology bases (TB), and style guides into AI translation.
The theme this time was not about having AI translate the source text as is, but rather how the output changes when you organize 'context'—such as the document's purpose, field, and reference terms or expressions—in advance.
Using translation challenges in fields like medicine and industry as examples, a series of methods were introduced for having AI perform document analysis, create style guides and glossaries, translate, and conduct quality checks. It was content that made one realize the importance of preparation and information design before translation.
Highlights
In the latter half of the discussion, the conversation expanded beyond the performance of the model itself to 'context engineering'—what information to provide to the AI and in what order—and 'harness engineering,' which involves designing the surrounding mechanisms.
There was also a lively exchange of opinions on practical issues, such as at what stage humans should verify the translation purpose, style guides, and glossaries generated by AI, and how to preserve expert knowledge as explicit knowledge.
Furthermore, the discussion on whether AI translation will simply replace existing translation work or open up new areas that have never been translated before was also impressive.
For actual prompt design, generated deliverables, and more in-depth discussions with participants, please be sure to watch the member-exclusive archive.
What we introduced here is just a small part of the session.
Please watch the archive for the heated discussion based on generative AI.
The full session and the actual archive are available exclusively for paid members of Translation Cafe (2,200 yen per month).
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