Machine Translation Quality Assessment

Project: Research project

Project Details


The MTQA project is a collaboration between Tilburg University and Aglatech14 (an Orbital14 company) aiming to develop a tool for quality assessment of machine translation (MT) for the translation industry. The overall objective of this project was to develop a tool (a proof of concept, PoC) that simplifies the quality assessment of machine translation (MT) engines, in terms of translation quality.

Layman's description

With the increase in machine translation (MT) quality over the latest years, it has now become a common practice to integrate MT in the workflow of language service providers (LSPs) and other actors in the translation industry. With MT having a direct impact on the translation workflow, it is important not only to use high-quality MT systems, but also to understand the quality dimension so that the humans involved in the translation workflow can make informed decisions. The evaluation and monitoring of MT output quality has become one of the essential aspects of language technology management in LSPs’ workflows. First, a general practice is to carry out human tests to evaluate MT output quality before deployment. Second, a quality estimate of the translated text, thus after deployment, can inform post editors or even represent post-editing effort. In the former case, based on the quality assessment of a candidate engine, an informed decision can be made whether the engine would be deployed for production or not. In the latter, a quality estimate of the translation output can guide the human post-editor or even make rough approximations of the post-editing effort. Quality of an MT engine can be assessed on document- or on sentence-level. A tool to jointly provide all these functionalities does not exist yet. The overall objective of the project is to develop an MT quality assessment (MTQA) tool that simplifies the quality assessment of MT engines, combining quality evaluation and quality estimation on document- and sentence- level.

Key findings

Quality Estimation, Quality Evaluation, Machine Translation
Effective start/end date10/01/2210/08/22


  • Machine Translation
  • Quality Estimation
  • Quality Evaluation
  • Quality Assessment


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