Build an MQM model for the task
Choose error categories and subcategories, severity levels, weights, the scoring method and the pass threshold for a given text type and service.
An online course for editors, QA specialists and translation managers. You build your own MQM-based error typology, annotate errors by category and severity, and calculate a quality score with a pass/fail threshold, working out RQS and CQS on training texts.
For anyone responsible for translation quality who needs to measure it and back up their findings.
Expect spreadsheets, error categories and calculations.
Choose error categories and subcategories, severity levels, weights, the scoring method and the pass threshold for a given text type and service.
Classify errors using the MQM 2.0 Core typology and assign a severity: Neutral, Minor, Major or Critical.
Calculate the raw score (APT → PWPT → RQS) and the calibrated score, using a threshold and scaling so that results are not capped at 100.
Use assessment for pre-delivery checks, vendor test translations, periodic translator evaluation and external quality audits.
Linguistic quality assessment scores a translation by counting objective errors against a chosen model. Four scenarios: pre-delivery checks; test translations; periodic and ad hoc evaluation of translators; LQA as a separate service and external audit.
Error categories and subcategories, severity levels, weights, the scoring method and the pass/fail threshold, and how they combine into a working assessment scheme.
Multidimensional Quality Metrics (MQM) was created in 2014 by the German Research Center for Artificial Intelligence (DFKI), building on LISA QA. The module covers configuring MQM for different services, text types and subject areas; MQM 2.0 Core and Full, and why Core is the one used in practice; and severity levels and penalty points, calculated as category weight × severity multiplier.
The uncalibrated Raw Quality Score (APT → PWPT → RQS) and the Calibrated Quality Score: RWC, maximum score, passing threshold, scaling factor. When to use non-linear formulas (samples above 5,000 words), and when to switch to holistic assessment on the Correspondence and Fluency scales.
Example: Terminology (weight 1) × Major (5) = 5 penalty points; then RQS = (1 − PWPT) × 100.
Theory and joint annotation. Participants keep access to the recordings.
Building an MQM model for a task, annotating errors, calculating RQS and CQS. Materials include an MQM master spreadsheet and sample LQA annotations.
The teacher reviews your annotations and calculations, flags debatable classifications and helps you calibrate the model.
Yuri Vodostoy, founder of INSIGHT, has taught Linguistic Quality Assessment and Translation Quality Metrics at Tomsk State University since 2024. This course is based on that university programme, which he has taught to two cohorts of master's students, adapted for translation teams and freelancers.

Participants receive a certificate of completion as standard.
The course is for people who already work in translation: editors, managers, translators. You don't need a maths background either; we explain RQS and CQS calculations from the ground up, with examples.
Editing corrects the text. LQA measures its quality: it records objective errors against a chosen model, assigns severity and produces a numerical score with a pass threshold. LQA can be a separate stage or a standalone service.
MQM is a general-purpose base metric that you can configure for different services, text types and subject areas. Most current models in LQA tools are built on it.
You build an MQM model, annotate errors and calculate scores on training translations, using an MQM master spreadsheet and sample LQA annotations.
We form groups as applications come in. Get in touch and we will send you the start date and the detailed programme.
Email edu@insgt.pro with the subject "LQA course", or message us on Telegram at @insgtmgr. We will confirm the dates of the next group and the participation format.