Quick Start Guide to the
Dynamic Quality Framework
Solving an Industry-wide Challenge
Dynamic Quality Framework Solution
● Dynamic, considering different content
types, purposes, audiences
● Standardized quality evaluation methods
applied to your workflow
● Benchmarking your performance against
● Translation activity tracking across
different CAT tools
● Real-time reporting
Quality Management Challenge
● Static, ‘one size fits all’ approach to
● Lack of an industry-shared quality
● No ability to benchmark against others in
● No integrated tracking solution across
different CAT tools
● Manual reporting process, use of
checklists and score-cards
● Productivity and quality measurement for
translation and review process
● Integration with translation tools
● Internal and external benchmarking
● Customized reporting in your own
reporting environment with Data
Quality Evaluation with DQF-MQM Error Typology
● Three review types: correction, error annotation, combined
● Eight main error types and thirty-two subcategories
● Pass/fail threshold per 1000 words
● Four error severity levels: neutral, minor, major, critical
● Ability to create review templates per content type, process type, etc.
Open API & Plugins
Most common CAT
tools & TMS
Translation & review
benchmark and trend
How does it Work?
Project Workflow and Reporting
● Translation phase
● Review phase reports
● Segment-level reports
● Internal: On the level of
user and organization
● External: Industry
● Shared metrics across
various user roles and tasks
● Each stakeholder gets
access to the data relevant
for their scope of work
● Transparent approach
On the project level, you can track such metrics as the average productivity rate, edit distance,
time spent, correction and error density. Use the available filters to see the results per target
language, vendor or segment origin category.
The Benchmark Reports allow you to compare your results against the averages in your
organization and in the industry.
Available: productivity, correction density and error density benchmarks.
The Trend Reports give you insights in performance over time.
Available: productivity, edit density and correction density trend reports.
For more detailed analysis, you can download Excel-based Segment-level Reports.
What Kind of Data does TAUS Collect?
● Language Data: source text and its matching target content.
● Metadata: all data associated with the Language Data, such as information indicating the
language pair, industry sector, content type and other pertinent information.
● Personal Data: name and email address of users who participate in the DQF project
*All data is collected on a consensual basis. The user who initiates the DQF workflow agrees
Where does TAUS Store DQF Data?
The DQF services are running on a single AWS EC2 instance. Amazon Elastic Compute Cloud (Amazon EC2) is a web
service that provides secure, resizable compute capacity in the cloud.
The DQF databases(API, statistics) are hosted on an AWS RDS instance. Amazon Relational Database Service (Amazon
RDS) is a web service that makes it easier to set up, operate, and scale a relational database in the cloud.
We have defined a virtual network (Amazon Virtual Private Cloud - VPC) where both instances have been launched
into. A VPC is an isolated portion of the AWS cloud where the communication between the two DQF instances is taking
● VPC Security - http://docs.aws.amazon.com/AmazonVPC/latest/UserGuide/VPC_Security.html
● Network security - http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/using-network-security.html
The availability zone in which these two DQF instances are located is the eu-west-1a EU (Ireland). Availability Zones are
distinct locations within a region that are engineered to be insulated from failures in other Availability Zones.
DQF White Paper: https://www.taus.net/academy/reports/translate-reports/taus-quality-management-
TAUS Terms & Conditions: https://taus.net/terms-conditions
Frequently Asked Questions