- Scalability: The TAUS HLP Platform is easily scalable to allow for large volumes of data to be processed by a big number of data annotators in a controlled environment
- Customization: The client requested customized data annotation features to maximize eciency and effectiveness. Our teams were able to create a solution that exactly matched the client’s specific requirements
- Community based on product affinity: A highly engaged community of HLP data annotators was recruited per language based on the workers’ product affinity
- Competitive cost structure: Thanks to the short supply chain in the TAUS HLP Platform, we were able to offer a competitive cost structure while ensuring fair compensation to the communities engaged, based on our Fair Cooperation Principle.
- Customized data tokenization based on client specifications
- On-screen explanation of the tags to use to annotate the data, including client-specific examples (1)
- Easy one-click tagging experience to optimize tagging speed and effort (2)
- Intuitive tag dragging feature to group connected tokens with the same tag (3)
- Customizable workflow to add multiple annotation/review steps as per client’s requirements
- Project level QA checks to efficiently handle consistency checks across the whole project (4)
TAUS Estimate API as the Ultimate Risk Management Solution for a Global Technology Corporation
Based on examples of texts from one of the largest technology companies in the world, TAUS generated a large dataset and customized a quality prediction model. The accuracy rate achieved was 85%.
Domain-Specific Training Data Generation for SYSTRAN
After the training with TAUS datasets in the pandemic domain, the SYSTRAN engines improved on average by 18% across all twelve language pairs compared to the baseline engines.
Customization of Amazon Active Custom Translate with TAUS Data
The customization of Amazon Translate with TAUS Data always improved the BLEU score measured on the test sets by more than 6 BLEU points on average and 2 BLEU points at a minimum.