Under Community Review

Smart TM Cleanup: A Lifesaver for Accidental XLIFF Imports!

Dear Trados Team,

I hope there is already such a feature, that I am not aware of, so please pardon me if there is one!

I’d like to suggest a feature that could be a real game-changer for both novice and professional translators using Trados Studio.

Imagine this: a translator — even a seasoned one — mistakenly imports poorly translated XLIFF files into a high-quality Translation Memory (TM). The result? A compromised TM filled with inaccurate segments that are hard to undo.

Currently, there’s no quick or elegant way to reverse such an action. That’s why I propose a new "Segment Cleanup" feature:
 Arrow right️ The ability to remove TM segments that match those found in one or more XLIFF files.
This would allow users to “roll back” accidental imports by identifying and deleting all segments that came from flawed XLIFF files.

Of course, best practice dictates using a temporary or “dummy” TM first and importing final results afterward — but mistakes happen, especially under deadlines. A feature like this would provide an extra layer of safety and flexibility.

I believe this would be relatively straightforward to implement and would significantly boost user confidence in managing their TMs with precision and peace of mind.

Looking forward to your thoughts!

Best regards,
Sameh

Cartoon of a worried TM file with bandages on a hospital bed, surrounded by medical staff holding a clipboard labeled 'XLIFF Segment Match Removal Protocol'. Text reads 'We need to remove the contaminated segments, stat!' and 'When bad translations slip into your TM, Trados performs emergency cleanup surgery!'.

Parents Comment
  • Thanks a lot Mohamad, Happy Feast, but I am talking about a larger problem where you can a lot of old entries, and you would not have time to look up dates or the like to delete the bad translation units from the TM, while you still have the bad xliff files that can be taken in one batch and get them deleted from the TM!

    All the best.
    Sameh

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