Translation memory (TM) is a database that stores and matches segments that have previously been translated, and then reuses them to aid translation. Below are some of the most frequently asked questions from translators about TM and how it works.

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Why would a customer use TM?

If they have a large volume of repetitive content, such as product descriptions, they may decide to use TM.

How does TM help the customer?

It can improve consistency, reduce costs and improve speed of their translations.

How does TM affect my rewards?

Rewards are automatically calculated and have been decided based on the level of effort required to review or edit a job. For segments within jobs that have no matches, rewards are unaffected.

When a match is found in the translation memory, rewards are adjusted only for the given segment based on the quality and length of the match. Adjustments are as follows:

  • New / Unique (0–74%, no/unusable match that requires full translation)
    • 0% reduction. i.e. no change
  • Fuzzy (75–99%, a partial match that requires minor edits)
    • 60% reduction. e.g. $1.00 > $0.40
  • Exact (100%, a perfect character-by-character match that requires spot checking)
    • 80% reduction e.g. $1.00 > $0.20
  • Contextual (101%, an exact match where both the preceding and following segments are also exact matches and requires spot checking)
    • 80% reduction e.g. $1.00 > $0.20
  • Repetition (a duplicate exact match in the same job that requires none/minimal work)
    • 100% reduction e.g. $1.00 > $0.00

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What’s the difference between a Repetition and an Exact (100–101%) match?

A repetition is a duplicate segment within the same job, but an exact match comes from the TM database of previously translated content.

Where can I see a breakdown of matches and associated reward adjustments?

  • We’re working on a solution to display this in the workbench

 

How do translations become part of the TM?

  • Each time a job is submitted, the translation is automatically added to the TM for a particular customer

 

Who is responsible for bad translations within a TM?

  • Translators are responsible for ensuring that they only use accurate matches
  • Gengo working on solutions to remove/clean poorly translated matches

 

How does Gengo police bad translations within the TM?

  • We're monitoring when TM matches are either used or edited so we can remove poor translations. Poor translations found in the TM will be deleted so they can’t be used in the future. This currently happens via a manual process but our engineering team are working to automate it
  • We're also working on introducing a community voting system to allow translators to quickly give direct feedback on match quality so we can remove poor matches in real time

 

Are there any keyboards shortcuts to help make using TM more efficient?

We recently added two new shortcuts, as requested by translators. We will be adding more in the future so please let us know what would be helpful to you.

  • View matches: cmd + shift + m
  • Insert the top match: alt + shift + m

 

Can you make the user interface for viewing TM matches and glossaries more efficient (e.g. less scrolling)?

  • We’re considering ways of improving this without increasing complexity

 

How are repetitions calculated?

  • Repetitions are based on all segments within an individual job (not across collections)

 

When will the TM “beta” period end?

  • Currently less than 1% of our customers are using TM. This number will continue to grow gradually over time and we’ll continue to make improvements as we receive feedback from both translators and customers.

 

Is there a glossary/concordance search feature?

  • Not yet, but we’re considering it

 

Is there a way to collaboratively add a term to a glossary?

 

Can Gengo introduce an automated QA function?

  • Yes—we’ve introduced the following automated checks which happen every time a job is autosaved and are working on more:
    • Misspelled word
    • Missing glossary term
    • Missing tag
    • Missing triple bracket
    • Missing number
    • Incorrect number formatting
    • Incorrect currency formatting
    • Max. characters exceeded
    • Empty target

 

Will Gengo consider adjusting TM rewards based on our feedback?

  • By introducing TM, many new customers have started working with Gengo who otherwise wouldn’t have chosen us due to their specific need for this feature. These customers have high volumes—and a consistent amount—of work that they are giving our translators across many language pairs. Because of TM, we have even opened up new language pairs that didn’t previously exist. Thus, giving new opportunities and work to Gengo translators.
  • However, we do always aim to pay a fair rate to our translators for all work that is required, whether translating or reviewing a match. As the volume of jobs using TM grows, we’ll continue to try and maintain a fair balance and will be constantly reviewing the situation.

 

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