Problems with Machine Translation Pricing Strategy
As the rate of Machine Translation (MT) adoption increases, a standardized method of pricing MT is being called
for by many industry professionals.
The commercialisation of Translation Memory technology in the early 1990’s revolutionised the localization
industry and led to increased productivity and translation performance. It also led to the introduction of a pricing
model which is still used by many LSPs (language service providers) today. This pricing model is based on fuzzymatch scores, where translation is charged at a rate based on the fuzzy-match score of a segment. This structure
has become an industry standard model and is used by most Project Managers to carry out costing and
scheduling of translation projects.
“The moment you make a mistake in pricing, you're eating into your
reputation or your profits.” - Katharine Paine
Machine Translation users have suffered until now due to the difficulty in pricing MT as a service. MT users have
been experimenting with different ways of pricing PEMT (Machine Translation Post-Editing), however a
standardized method has not yet been established. Some LSPs are using fixed charges, such as calculating hourly
rates or pricing based on a fixed number of words, however this method lacks precision and transparency and
is not a sufficient cost calculation method to drive the wide scale adoption of Machine Translation.
Solution…
What if we had a way of integrating Machine Translation quality estimation with fuzzy match scores? That way
LSPs could develop a standardized method of pricing. Obviously, there are factors outside of these Automatic
Quality Estimation scores that need to be taken into account but having a clear and accurate estimation of the
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post-editing requirement of Machine Translation output means that Project Managers will be able to scope
projects involving MT much more accurately.
KantanAnalytics is a technology which enables users of Statistical Machine Translation to estimate MT quality in
the same manner as traditional translation projects – it is based on fuzzy-match scores. The quality estimation
scores applied by KantanAnalyics are expressed as a percentage - a higher score correlating to a higher quality
translation segment requiring less post-editing.
Project Managers are using this Analytics technology to improve efficiency and management of projects
involving Machine Translation. PM’s can view quality analysis reports in their Dashboard on KantanMT.com or
download it as a Microsoft Excel file, which is in the same format as a fuzzy match report.
KantanAnalytics technology attempts to facilitate the addition of Machine Translation into LSPs service offering
by giving Project Managers a structural method of quality analysis and pricing. PM’s can prioritise segments for
translation based on the client’s specific requirements. ‘Fit-for-purpose’ translations can be completed very
quickly as project managers can instruct post-editors to ignore all segments over a pre-determined a percentage
score. These PM aids make it easier to develop pricing packages for a variety of different Machine Translation
use cases and illuminate the uncertainty from both clients and also translators – making everyone more
confident in the use of the technology.
Machine Translation is today more accessible than ever. Increased development in technologies to bring MT
mainstream, coupled better quality, more efficient Machine Translation development platform and a growing
acceptance from both users, business and the general public makes it look like MT is only getting better!
Get in touch today to set up a private demo…
T. +353-1-700-7874 | E. [email protected] | www.KantanMT.com
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