AI is currently transforming every industry, and the translation industry is at the forefront. While human expertise remains irreplaceable, AI tools are being integrated into workflows to boost efficiency and accuracy. In this post we’ll walk you through a modern translation workflow and explain in simple terms how AI is being integrated.
Step 1: Tapping into Translation Memory (TM)
The journey begins with translation memory (TM). This technology acts as a digital vault, storing previously translated phrases and sentences. When a new project arrives, the TM scans it for matches with past translations. Identified matches are readily available to the translator, saving them time and effort while ensuring consistency across projects.
Step 2: The Power of AI Translation Engines
Next comes the magic of AI translation engines, also known as machine translation (MT). These tools utilize complex algorithms to analyze vast amounts of bilingual data. This allows them to "learn" how to translate text, often producing surprisingly accurate results, especially for factual content.
One important consideration here is that certain MT engines are better for certain types of translation. Which translation engine to use will depend on the language and the subject-matter (is it general business content or is it complex technical content). For example, Google NMT may provide better results for Portuguese technical content, whereas DeepL might be better suited for general business content to be translated into French.
While the quality of MT engines has improved dramatically, and is improving at a rapid pace, they aren't quite perfect. Nuances of language, cultural references, and complex sentence structures can still pose challenges. This is where the next step comes in.
Step 3: Quality Estimation - The AI Editor's Assistant
Here's where AI truly shines as a collaborator. Quality estimation (QE) technology analyzes the MT output to predict its accuracy level. It does this by considering various factors:
Linguistic fluency: The tool checks for grammatical errors, unnatural sentence structures, and overall readability.
Terminology consistency: It ensures the translated text aligns with the project's specific terminology and style guide.
Domain expertise: For specialized content like legal, technical, or medical documents, QE technology can identify potential inaccuracies related to the specific domain.
By analyzing these aspects, the QE tool assigns a score reflecting the estimated quality of the MT output. This score informs the human translator where to focus their editing efforts.
Step 4: Human-in-the-loop (HIL) - The Final Step
The final step in our translation workflow involves a human translator, the ultimate quality control. Armed with the translation memory matches, MT pre-translated text, the QE score, and the context of the project, the translator refines the translation. They ensure the translation conveys the intended meaning accurately, reads naturally, and adheres to cultural nuances.
The Synergy of Human and Machine
Integrating AI into the translation workflow doesn't replace human translators; it empowers them. By automating repetitive tasks and providing valuable insights, AI tools free up translators' time to focus on the complexities of language and cultural adaptation. This collaborative approach leads to faster turnaround times, improved consistency, and ultimately, exceptional translations that resonate with global audiences.
Learn More
The workflow we detailed in this post represents a simple depiction of how AI can be integrated into a basic translation process. This process can serve as an excellent foundation for a wide variety of translation projects, and can be easily expanded and modified to match your organization’s specific needs and requirements. If you’re interested in learning more about what AI can do for your translation workflow contact us to find out!