Q: How do AQE and APE improve translation workflows?
A: They automate quality checks and corrections, reducing human workload while maintaining accuracy, speed, and consistency across large-scale multilingual projects.
Q: What results did the LanguageLine and TAUS pilot achieve?
A: Over 50% of machine-translated segments required no edits, cutting post-editing time by up to 80% without sacrificing quality.
Q: Does AI replace human translators in this model?
A: No. AI handles repetitive tasks, while human linguists ensure accuracy, nuance, and context remain central to translation quality.
Roadmap to Integration and ROI.
We were joined by experts from TAUS, a global leader in language data and AI-powered
translation tools: Simona Beccaletto, Head of Go-to-Market Strategy, and Amir Kamran,
Solutions Architect. Amanda Downing, Solutions Architect at LanguageLine , rounded out the panel.
Drawing from a collaborative proof-of-concept study between our organizations, the panel
revealed how two emerging technologies—Automated Quality Estimation (AQE) and Automatic Post-Editing (APE)—are transforming translation workflows and delivering measurable ROI.
AQE and APE help translation clients translate more content, in more languages, at lower cost, without sacrificing quality or control. By automating routine review and improving MT output before a human ever sees it, these tools make localization faster, more scalable, and better aligned with business priorities.
Here are the three critical insights from their discussion:
1. AQE Lets You Focus on Human Effort Where It Matters Most
Many teams still post-edit every MT output, regardless of whether it needs revision. That
creates unnecessary work and slows turnaround. AQE changes this by evaluating machine
output upfront and identifying segments that are already good enough.
“The idea of quality estimation is exactly that,” Kamran said. “You can build a model that takes the source sentence and the machine output and then decide whether it’s a true representation or not.”
In the LanguageLine pilot, more than 50% of the segments required no editing. By trusting AQE to make that call, teams can bypass low-impact review and reserve human attention for segments that truly need it. The result is faster delivery, less fatigue, and fewer errors.
2. APE Adds a Second Layer of Automation Without Cutting Humans Out
For segments AQE flags as needing improvement, APE provides an automated fix before a
human ever sees the content. These improvements come from large language models (LLMs) prompted with terminology, style guides, and context.
“All of the post-edited segments had a new AQE score that was at least equal to or higher than the original,” Downing said. “That was good to see.”
Downing noted that while the pilot used a generic model, the next step is to apply custom
prompts and domain-specific inputs to refine performance. With that in place, APE can deliver even stronger results, allowing linguists to work more efficiently without sacrificing quality.
3. Human Expertise Remains the Backbone of Translation Quality
Even as automation expands, expert oversight is still essential. This was a shared view across all three panelists.
“The backbone of all this pipeline — all these models — is human intelligence. It’s nothing
magic,” Kamran said.
He explained that AI should handle the repetitive or low-risk segments, not replace the translator entirely.
Beccaletto, who leads global positioning strategy at TAUS, emphasized the value of
understanding how the tools work.
“The most important thing is not being afraid of the technology. Understanding it better enables every part of the chain to use it better and to know what to expect.”
Downing added that AI's role is not to replace, but to reshape. “The results do show that human expertise is still essential,” she said. “It aligns with our general experience when we’re looking at automated translation.”
The Bottom Line on AQE and APE
AI-powered workflows like TAUS Epic are not about removing people from the process. They are about giving them the tools to focus on where they make the most impact. AQE filters out segments that do not need revision. APE improves what can be fixed automatically. Humans step in where quality, nuance, or risk demand expert attention.
For teams balancing speed, scale, and consistency, this approach is already proving effective.
As Kamran put it, “You can save up to 80% of your post-editing effort, and it’s not just a
gimmicky statement. It’s happening now.”
To learn more, contact your LanguageLine Business Development Manager or email us at
translation@languageline.com. You can also visit our website to explore our AI Translation solutions, including our self-service Translation Portal.