How would you implement a translation memory workflow to streamline localization?

How would you implement a translation memory workflow to streamline localization?

• A. An approach that relies on live human translation without memory.
• B. Maintain a translation memory database to reuse previously translated segments, apply fuzzy matching, and update memory post-translation.
• C. Use machine translation without any memory features.
• D. Translate only new content and ignore legacy translations.

Answer: (B)

Translation memory workflows strive to maximize reuse of previously translated segments to speed localization and maintain consistency. The core idea is to maintain a translation memory database that stores source text alongside approved translations. As new content is processed, the system searches for matching or similar segments and suggests translations, using fuzzy matching to capture partial similarities and preserve terminology. Once a translation is approved, the memory is updated with the new segment, continuously enriching the repository so future work can reuse it. This approach cuts workload, reduces errors, ensures consistent terminology across languages, and speeds up delivery by leveraging proven translations across projects and updates. Other approaches miss these benefits: relying on live human translation without memory leads to repetitive work and slower delivery; using machine translation without memory forgoes reuse and post-edits that align with established terminology; translating only new content while ignoring legacy translations discards a valuable asset and can create inconsistency and higher costs.

Translation memory workflows strive to maximize reuse of previously translated segments to speed localization and maintain consistency. The core idea is to maintain a translation memory database that stores source text alongside approved translations. As new content is processed, the system searches for matching or similar segments and suggests translations, using fuzzy matching to capture partial similarities and preserve terminology. Once a translation is approved, the memory is updated with the new segment, continuously enriching the repository so future work can reuse it. This approach cuts workload, reduces errors, ensures consistent terminology across languages, and speeds up delivery by leveraging proven translations across projects and updates.

Other approaches miss these benefits: relying on live human translation without memory leads to repetitive work and slower delivery; using machine translation without memory forgoes reuse and post-edits that align with established terminology; translating only new content while ignoring legacy translations discards a valuable asset and can create inconsistency and higher costs.