Translation Memories

Translation Memory Quality

Content is machine translated from English by Phrase Language AI.

Translation memory (TM) is essential for producing consistent translations and can dramatically reduce translation costs. If a TM is not set up correctly and maintained, inconsistent and poor quality translations are produced.

Follow these rules to improve TM quality:

  1. Choose trusted providers

    Have a group of trusted providers (linguists/vendors) who deliver high-quality output that is saved to the master TM. When working with a provider for the first time or with someone whose output quality varies, consider using a secondary working translation memory where they can commit segments and keep the master TM in read-only mode. Use the master TM in Read and Write mode in later workflow steps where review is performed.

    Preventing content of questionable quality from being included in a TM is easier than removing it later.

    Suggested TM configuration:

  2. Add context information to source files

    Context information allows linguists to better understand content they are translating and improves the quality of the translation. There are different options for providing context such as attaching assets as reference files to projects or adding them on the segment level. For file formats with context key and notes properties, information can be displayed on segment level in a CAT tool. Some editors can display animations and graphics from attached external links.

  3. Lock segments with high-quality matches

    Pre-translating content from translation memory and locking high-score matches (context matches) prevents unwanted changes in the TM. Excluding locked segments from analysis and quotes shared with a provider reduces translation volume and costs.

  4. Perform quality assurance and spell checks before confirming to TM

    Misspellings, missing tags, incorrect punctuation are easily overlooked. automated quality assurance (QA) checks help with this. Advanced QA checks are also able to verify if correct terminology has been used—ensuring translation consistency. Some tools enable segment-level QA which won’t allow the provider to confirm segments and save them into the TM if quality assurance errors have been found. In case segment-level QA is not available (and the check is performed at the end of the localization process), use the working TM approach.

  5. Perform linguistic quality assurance (LQA) evaluations

    LQA evaluation is used to measure and qualify the translations and errors produced. It evaluates translation quality and provides constructive feedback to the provider.

  6. Update your TMS with any changes that happen outside of your translation management system.

    If linguistic edits take place in the native format or in a content management system, they are not saved to the TM and will be overwritten by future submissions of the same content unless the TM is updated. In such a scenario, update the TM manually.

  7. Close the feedback loop

    Discuss the quality of delivered translations with the provider and allow them to see the changes made to their work. It is important to clarify expectations and review detected issues to avoid encountering them again in the future.

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