Machine translation (MT) is increasingly used in language projects to increase the speed, efficiency and consistency of results. However, such translation tools come with compromises in quality and accuracy, which language experts have to mitigate.
The quality of MT output can vary greatly from one project to the next, which can make it difficult for companies to know what to expect. This is where machine translation quality estimation (MTQE) comes in, calculating the expected quality of MT output for any given project – so you can plan more effectively.
Machine translation quality estimation (MTQE) is an automated method of predicting the quality of results produced by machine translation tools. It replaces traditional evaluation methods to predict quality without manual evaluation from language experts.
Machine Translate, a non-profit organisation dedicated to machine translation, defines machine translation quality estimation as follows:
“Machine translation quality estimation… is the task of automatically predicting the quality of machine translation output… It uses machine learning to provide predictions that are relatively accurate at the segment level.”
MTQE uses machine learning to analyse the source material, source language and target language(s) to calculate the expected quality of machine translation tools available. It can help language experts predict the effectiveness of machine translation for different projects and develop a suitable translation process.
For example, a lower MTQE score may inform translation teams to dedicate more resources to post-editing to achieve the required standard. Alternatively, a higher score could indicate that fewer translators are required to work on a specific project, thanks to a higher expected accuracy from the machine translation tools.
As the machine learning technology powering MTQE systems matures, it benefits translation projects more and more. At the same time, AI-powered machine translation is improving all the time, which means both technologies will continue to have a bigger impact.
As a result, one of the key benefits of machine translation quality estimation is that it will help language professionals track the progress of machine translations as their results improve further.
On a case-by-case basis, MTQE also benefits projects in the following ways:
- Quality estimation: The core purpose of machine translation quality estimation is to predict the quality of machine translation before the project begins.
- Automated predictions: By using machine learning models, MTQE can automatically predict the quality of machine translation tools without the input of any language professionals.
- Granular analysis: The latest MTQE systems can estimate the quality of translations at the word, phrase and sentence levels.
- Issue prediction: MTQE tools can now identify potential translation issues before they even occur – e.g. problematic words that MT often mistranslates or grammatical structures it struggles with.
- Project planning: With reliable quality estimates, language experts can plan translation projects more effectively.
- Personnel assignment: Predicting the quality of MT results helps to determine how many translators and other language experts will be needed for a given project.
- Post-editing: With quality estimate scores, agencies and language experts can allocate the right amount of time for post-editing.
- Accurate pricing: By helping agencies and language experts plan projects, MTQE also helps us to price projects more accurately.
Reliable project planning is essential for budgeting and scheduling. Without a system for calculating the quality of machine translation for each project, it can be challenging to know how much time, money and manual input the project will need.
This is especially problematic for large projects, projects that rely heavily on machine translation and companies that have to plan, budget and schedule projects throughout the year. In these instances, any inaccuracies are multiplied, increasing the risk of budgeting issues, missed deadlines and reduced profits.
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