Glossary

Post-Editing

Post-Editing is the process where a human linguist reviews, corrects and refines content that has been translated by a machine translation (MT) system. It combines the speed of automation with the critical thinking and cultural fluency of a professional translator to produce a final output that meets specific quality objectives.

Description

In the era of Neural machine translation (NMT), the role of the translator is evolving. Post-Editing transforms the translation workflow from "creation" to "validation and refinement." Instead of translating a source text from scratch, the linguist works with a pre-translated draft generated by AI. Their task is to bridge the gap between raw machine output and human expectations.

Post-Editing is not a one-size-fits-all process. It is typically categorized into two levels based on the desired outcome: Light post-editing (focusing on comprehensibility and correcting major errors) and Full post-editing (ensuring publication quality, stylistic fluency, and cultural appropriateness). The success of Post-Editing depends heavily on the quality of the raw MT output. When engines are trained on domain-specific data, the initial translation is more accurate, allowing the post-editor to focus on nuance rather than basic corrections. This Human + Technology workflow allows organizations to translate vast volumes of content that would be cost-prohibitive using traditional human translation, without sacrificing the safety net of human review.

Example use cases

  • Technical: Processing thousands of pages of user manuals, support articles or product specs where consistency is key.
  • Communication: Translating internal emails, news updates or crisis communications that must be distributed immediately.
  • Retail: Localizing extensive product catalogs where full human translation would delay time-to-market.
  • Support: Enabling multilingual chat or ticketing systems where rapid understanding is more important than stylistic perfection.
  • Legal: Reviewing massive datasets of foreign-language documents to identify relevant files for litigation.

Key benefits

Speed
Increases productivity significantly compared to translation from scratch.
Efficiency
Reduces the cost per word, allowing budgets to cover more languages or content types.
Scalability
Handles spikes in volume without the linear increase in resource requirements associated with human-only translation.
Quality
Ensures that even automated content is verified by a human expert before publication.
Quality
Feedback from post-editors is often used to retrain MT models, improving future performance.

RWS perspective

At RWS, post-editing is a strategic capability that brings together our linguistic heritage and our AI leadership. We do not view it simply as "fixing" machine output; we view it as Human-in-the-Loop (HITL) collaboration.

Our approach utilizes Language Weaver to generate secure, high-quality neural translations, which are then refined by our global network of subject-matter experts using Trados. We align the level of post-editing (Light or Full) to the content's purpose and audience. This ensures our clients invest their budget where it matters most – applying deep human expertise to creative or regulated content while leveraging automation for efficiency on informational assets.