The Right QE Solution for Every Localization Architecture

The TAUS QE engine is accessible via the TAUS EPIC API. It features plug-and-play compatibility and can be integrated within a few clicks into industry-standard translation management systems like Phrase, memoQ and Blackbird. For custom environments, our developers provide comprehensive documentation and direct integration support.

Currently, the TAUS QE model evaluates translation accuracy and fluency at the segment level. While highly effective at flagging linguistic errors, semantic distortions, and structural issues within sentences, it does not fully track context or meaning dependencies across long, multi-paragraph documents. This is an area of continuous improvement, and human oversight is recommended for deeply context-heavy content.

The ideal dataset contains a "before and after" mix: the original raw MT output paired with the final, human post-edited or reviewed translation. This explicit pairing teaches the model exactly how your internal linguists detect and fix errors. Providing consistently followed corporate glossaries, term lists, and a brief summary of your translation style guidelines will further refine the model’s performance.

You do. Unlike Specialized or Generic models, a Customized QE model built using your enterprise data assets is securely hosted and fully owned by the party requesting it. Your data and the resulting custom parameters are strictly protected and never leaked into general training pools.

Once you deliver your structured training assets, the TAUS engineering team handles the processing, architecture training, and optimization. A fully customized enterprise model is typically ready for validation and delivery within 2 to 4 weeks, depending, of course, on the size of the project and the number of custom models required.

Extremely reliable. In our March 2025 Benchmarking Report, the TAUS QE model achieved a Spearman Rank Correlation of 0.7 or higher against native language experts across all tested language pairs (German, Spanish, French, and Italian). A correlation of 0.7 indicates a powerful, highly trustworthy alignment with human evaluation standards.

While the optimal threshold depends entirely on your specific domain and risk tolerance, our benchmarking data demonstrates that an F1 score threshold of 0.86 is the mathematical sweet spot for separating flawless translations from those requiring human intervention. In general practice:

• Scores > 0.9: Excellent quality; safe to bypass human review and publish immediately.

• Scores 0.88 to 0.90: A minor gray area; acceptable for low-risk content but worth monitoring.

• Scores < 0.88: Likely contains errors; recommended for automated routing to human post-editors.

Yes. Automated QE is a powerful efficiency companion, but human review remains essential for creative, marketing, and literary texts where tone, humor, or cultural nuance are highly subjective. Additionally, human validation is always required for high-consequence medical, legal, or regulatory content where minor translation errors present real-world safety risks.