Post-editing Analysis

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Introduction

The post-editing analysis in Memsource Cloud extends the traditional translation memory analysis to also include machine translation and non-translatables (NT). It analyzes the MT and NT post-editing effort for each segment and compares the MT and NT output with the final post-edited translation (edit distance). Therefore, if the MT or NT output was accepted without further editing (the linguist did not need to change it at all), it would come up as a 100% match in the analysis.

If, on the other hand, the linguist changes the MT or NT output heavily, the match rate will be close to 0%. The score counting algorithm is identical to the one that we use to calculate the score of translation memory fuzzy matches. The only difference is that the post-editing analysis is based on the target. Therefore, the post-editing analysis must be, quite naturally, launched after the post-editing job has been completed.

A sample post-editing analysis:

AI-NTs Post-Editing analysis cropped.png


The post-editing analysis has three main components, which are combined: translation memory analysis, machine translation analysis and non-translatables analysis.

Translation Memory Analysis

  • When a user clicks in a segment, the current translation memory hit gets saved for that segment and is later used to calculate the translation memory match against the final post-edited translation
  • This means that if a user finds that a 100% TM match needs to be edited and modifies it accordingly, it will come across as a fuzzy match in the post-editing analysis (if the option Analyze TM post-editing has been selected)
  • For this reason in-context matches are not supported in the post-editing analysis, as each match is in-context by default
  • Because the analysis is based on TM matches that were available to a user in real time (or simply at the time of translation), it can also be very well used in a scenario in which multiple translators work in the same project, sharing the same TM and contributing to the TM as well as retrieving matches from it. The post-editing analysis will show for each segment who re-used matches from TM and who contributed them to the TM.

Machine Translation Analysis

  • When a user clicks in a segment, machine translation gets saved for that segment and is later used to calculate the machine translation match against the final post-edited translation.

Non-translatables Analysis

  • When a user clicks in a segment, a non-translatable match gets saved for that segment and is later used to calculate the non-translatables match against the final post-edited translation.