How Translation AI Improves Quality and Cuts Post-Editing Costs

Summary

Q: What problem does translation AI like AQE and APE solve?

A: AI helps organisations translate more content quickly and affordably while improving quality. It reduces editor fatigue and ensures localisation scales across global markets.

Q: How did LanguageLine and TAUS EPIC perform in testing?

A: In software, EPIC’s AQE accuracy matched human reviewers with error rates as low as 1 per cent. APE improved two-thirds of edited segments, boosting overall quality scores.

Q: What is the main takeaway about translation AI for global businesses?

A: AI works best as a partner. AQE and APE cut costs, speed workflows, and support linguists, but human expertise remains critical for context and nuanced translation.

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Testing the Power of AQE and APE with TAUS EPIC

This blog is part of a LanguageLine series where our Solutions Architects share real-world insights from client implementations, platform evaluations, and technical research.

Can AI Help Us Translate More Without Compromising Quality?

The language services industry faces the same tension: how to use AI to handle exponentially more content, faster and cheaper, without sacrificing quality.

Demand for localisation is skyrocketing, but budgets and timelines aren’t expanding to match. Adding more human translators doesn’t scale. That’s why we’re investing in technologies like Automated Quality Estimation (AQE) and Automated Post-Editing (APE). These tools can predict translation quality and refine output before humans ever see it.

But theory and practice differ. To test what’s possible, we partnered with TAUS and their AI-powered platform, EPIC.

AQE and APE: Why They Matter

Post-editors often face machine-translated documents without knowing which sentences need serious work. AQE changes that by scoring each segment, creating a triage system so editors know where to focus.

APE goes further by cleaning up obvious MT errors - grammar, terminology, and structure - before human review. The payoff: consistent output, faster turnaround, and less editor fatigue. With content volumes exploding, intelligent automation is becoming essential.

Why We Tested TAUS EPIC

While building our own AQE, we evaluated third-party options. TAUS’s EPIC stood out for combining AQE and APE in one solution. Their case studies suggest EPIC can reduce post-editing costs by up to 80%.

We knew a generic test wouldn’t match a fully customised deployment, but it could provide benchmarks.

Our Testing Approach: Software and Health Care

We tested two high-stakes domains: software and health care.

Foundation: We used approved, human-translated content as a benchmark, then machine-translated 500 segments per domain.
Work flow: Segments went through AQE scoring, with a high threshold of 1.0, ensuring every segment also passed through APE.
Validation: Human linguists then reviewed both raw MT and APE-enhanced output for accuracy and fluency.

Key Findings

AQE Accuracy: EPIC’s AQE aligned strongly with human evaluation for software. German and Italian reached error rates as low as 1%, Spanish at 3%, and French at 5%. TAUS benchmarks show that an AQE threshold of 0.85 identifies “good” content 96% of the time.

Health care was more challenging. Spanish error rates reached 20%, mainly because EPIC evaluates segments individually, while humans consider broader context. Context remains a critical challenge in segment-based MT workflows.

APE Evaluation: EPIC auto-edited about 10% of segments. Two-thirds of those improved enough that human reviewers judged them equal or superior to raw MT. Every post-edited segment scored higher on AQE, showing consistent quality gains.

Lessons from the Field

Content and Language Matter: EPIC excelled in structured software and performed best in Germanic and Romance languages. Deployment strategies should reflect these strengths.
Human Review Remains Essential: Algorithms miss nuance and context. AQE and APE increase efficiency but require testing, oversight, and calibrated thresholds.
AI Works Best as a Partner: EPIC shows real promise, but it delivers maximum value when paired with human expertise.

From Testing to Implementation

Benchmarks are useful, but real value comes from deployment. Our roadmap focuses on:

Integration: Exploring how AQE and APE can connect to production workflows through CAT tool plugins or orchestration platforms like Blackbird.io.
Optimisation: Testing custom prompts and tuning models to improve results, especially for document-level translation strategies and hybrid workflows.

Strategic Implication: Human-AI Collaboration

This project reinforced a core belief: AI doesn’t replace linguists; it empowers them. EPIC and similar platforms make human translation smarter and faster when integrated thoughtfully, with clear awareness of their limits.

We’re continuing to test, tune, and expand these capabilities, always keeping human expertise at the center.

Curious About AQE, APE, or AI-Driven Workflows?

If you’re evaluating platforms like EPIC, designing a risk-based strategy, or building a custom MT program, our team can help. Contact your LanguageLine Business Account Manager or email us at translation@languageline.co.uk

Amanda Downing is a Solutions Architect with LanguageLine Solutions.