Translation Edit Rate (TER)

Catalogue of Tools & Metrics for Trustworthy AI

These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.

Overview Tools Metrics About the catalogue

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Translation Edit Rate (TER), also called Translation Error Rate, is a metric to quantify the edit operations that a hypothesis requires to match a reference translation.

Related use cases :

Rethink about the Word-level Quality Estimation for Machine Translation from Human Judgement

Uploaded on Nov 1, 2022

Word-level Quality Estimation (QE) of Machine Translation (MT) aims to find out potential translation errors in the translated sentence without reference. Typically, convention...

About the metric

You can click on the links to see the associated metrics

Objective(s):

Fairness
Performance
Explainability

Purpose(s):

Forecasting/prediction
Recognition/object detection

Lifecycle stage(s):

Operate & monitor
Verify & validate

Target users:

Data scientist
Developer

Risk management stage(s):

Define
Assess
Treat

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Disclaimer: The tools and metrics featured herein are solely those of the originating authors and are not vetted or endorsed by the OECD or its member countries. The Organisation cannot be held responsible for possible issues resulting from the posting of links to third parties' tools and metrics on this catalogue. More on the methodology can be found at https://oecd.ai/catalogue/faq.