Machine Translation Post-Editing Workflow with AI Integration

Machine Translation Post-Editing Workflow for Non-Fiction Titles

1. Initial Preparation

1.1 Define Project Scope

Identify the specific non-fiction titles to be translated and establish the target languages.

1.2 Select AI Translation Tool

Choose an appropriate AI-driven translation tool, such as:

• DeepL Translator
• Google Cloud Translation API
• Microsoft Translator

2. Machine Translation Phase

2.1 Upload Source Content

Input the original non-fiction text into the selected AI translation tool for initial machine translation.

2.2 Generate Translated Output

Obtain the machine-translated text and review for initial quality assurance.

3. Post-Editing Phase

3.1 Assign Post-Editors

Designate qualified linguists or subject matter experts to perform post-editing on the machine-generated text.

3.2 Conduct Post-Editing

Post-editors review the translated content for accuracy, fluency, and adherence to the original meaning. Tools to assist in this phase include:

• SDL Trados Studio
• memoQ
• Smartcat

3.3 Quality Assurance Checks

Implement a quality assurance process to ensure the final text meets publication standards.

4. Final Review and Approval

4.1 Review by Subject Matter Expert

A subject matter expert reviews the edited content to ensure technical accuracy and relevance.

4.2 Final Approval

Obtain final approval from stakeholders before proceeding to publication.

5. Publication

5.1 Format for Publishing

Prepare the final text for publication, ensuring it is formatted according to the publishing standards.

5.2 Distribution

Distribute the translated non-fiction title through appropriate channels, including digital platforms and print.

6. Feedback and Improvement

6.1 Gather Feedback

Collect feedback from readers and stakeholders regarding the translation quality.

6.2 Analyze and Refine Workflow

Utilize feedback to refine the workflow process and improve future translations.