Hi there,
I think it would be really helpful to use TM to perform QA check to check 90~95% match rate sentences.
Is there any way to do that?
Hi there,
I think it would be really helpful to use TM to perform QA check to check 90~95% match rate sentences.
Is there any way to do that?
Those fuzzy segments that been confirmed but not modified will be reported as inconsistencies in source when checking the TM and bilingual files as Ongoing Translation.
Hi omartin,
Thank you for your message.
āInconsistency in Sourceā would only find those have same target sentence.
What if I would like to check if certain target sentences follow the TM consistency?
Example:
[TM]
Source: Jāai vu un stylo sur la table.
Target: I saw a pen on the table.
[Translated File]
Souce: Jāai vu un pomme sur la table.
Target: I saw there is an apple on the table.
From above, you may see the target sentence does not follow āI saw XX on the tableā.
Is there any way to check the situation?
To get inconsistencies in source, the target text must match.
To detect segments that may have only a different word but have not been translated in the same style, I would use a checklist such as the following one:
Source: "Jāai vu un <[:letter:]+> sur la table\."
Target: -"I saw an? <[:letter:]+> on the table\."
Hi,
Thank you for your regex!
Itās very helpful. However, I need to adjust the pattern based on specific sentences.
I look forward to having the similar feature like I proposed in the upcoming updates.
Is it possible to set xBench so that it detects inconsistency between segments even if their match is lower than 100%?
Let me provide an example.
I would like. e.g., to have xBench telling me that there is an inconsistency between:
Hi Davide,
To get inconsistencies in target, the source text of both segments should be the same.
To get inconsistencies in source, the target text of both segments should be the same.
If the target and source text of both segments is different, this issues can only be checked via checklist entries.
Thank you! Your advise helped me
Thank you very much for your feedback.
Davide