Use best-fit MT & reduce the need for human review, enabling rapid assessment and deployment of accurate machine translation models. DeMT™ Estimate API seamlessly connects to your content workflows and provides real-time quality scores customized with your data so the results match your organization for accuracy, tone, and authenticity.
Integrated in
Make informed business decisions
Does a particular MT engine produce better output than another? Is a certain language pair consistently better translated through this or that engine? Now you’ll gather data over time and know the answer.
Set up condition-based workflows
Real-time quality prediction puts you in the driver’s seat: you can set a threshold above which segments are good to go, to reduce time and money spent on post-editing.
Benchmark MT engine performance and know when to post-edit
Find out if an MT engine consistently outperforms others for a particular content type or language pair, and route that content to the right engine.
Choose the right MT engine for your specific needs
Scores are generated from state-of-the-art models like sentence embeddings and the COMET framework.
Models can be trained on your data to adapt to your own standards.
Scores are returned in real-time. Handles any volume effectively.
Plans start as low as 20 M. characters per year, with room to grow.
Start with a simple API call and relax while we handle the rest.
Leverage our simple REST API to submit requests and receive scores seamlessly with on-call technical assistance from our experts.
Choose what score works best for you to compare MT engines.
TAUS QE Score
Proprietary AI system that evaluates how close in meaning two sentences are, and returns a score between 0-1. The higher, the better.
COMET Score
Custom Score
To find out how our quality estimation service could fit within your existing workflows and setup, sign up for a free trial in the Estimate API sandbox environment. You can to test the API, obtain test scores and build your own integration (or use an existing integration). A free trial sandbox account gives you access to our generic model, and includes 500,000 characters across 4 language pairs: EN-DE, EN-ES, EN-FR and EN-IT.
Contact us to get access
TAUS QE is a semantics-based quality score, telling you how close in meaning the two segments are. We use TAUS sentence embedding models in order to calculate this similarity score. For our quality estimation, we offer generic and custom models.
Generic QE Model
Our generic QE model is trained on 100+ languages. See the full list here. For our generic model, we offer the TAUS QE or Comet scores. The generic model is available off-the-shelf.
Custom QE Model
TAUS creates custom models based on your unique content. In order to do this, we collaborate closely with you to create a high-quality dataset with which we train the model. It is possible to train a single model to work for various language pairs or domains, however, the best results are obtained by training custom models that are both domain- and language-pair specific.
At STP we have found the online course in machine translation post-editing designed by TAUS a very informative as it tackles the challenges which our linguists have been facing when post-editing the MT assignments that come from our clients. Also, the TAUS post-editing course provides a comprehensive summary of MTPE, and gives its users the opportunity to test their post-editing skills in practice and track the time spent on post-editing, which is why all of STP’s translators and project managers, as well as some of our regular freelance translators, have completed the e-course as part of their continuing professional development.
Translation Technology Specialist at Sandberg Translation Partners Ltd
Our company is focused on translation and post-editing in the CEE languages. We understand this is the right time to offer PEMT services to our clients and we are aware of the fact that post-editing requires special skills and competencies from post-editors. That is why we have decided to use the TAUS PEMT course. This course gives our employees both a theoretical and a practical overview of PEMT. It is not only important to have post-editors with necessary skills, but at this relatively early stage of PEMT it helps us to persuade our post-editors that there is a possibility to find a win-win strategy for PEMT processes.
The TAUS course has informed me of the usefulness of Machine Translation and helped me readjust my thinking from revising translations for publishing to revising machine translated output in order to train an engine. At first, I over-edited the content as if revising for publishing, and that was quite time-consuming. The TAUS course explained how to evaluate and edit machine translated output to train an engine without over-editing or under-editing the content.