Analysis: Machine Translation Usage in Memsource Cloud

Memsource was a proud sponsor of the annual Conference of the European Association for Machine Translation (EAMT) on May 28-31, 2017 in Prague. Thanks to the mix of participants from both academia and translation industry, the conference sparked several interesting discussions, including the use of machine translation (MT) in professional translation.

As a sponsor and participant, Memsource presented a poster at the event which analyzed the use of MT in Memsource Cloud. Here’s what we found.

Quality Translations: Finding Success By Becoming The Whole Package

“Machines can translate the words, but the human is needed to translate the meaning, to add the context, and to breathe life into the words,” says Andreea Olteanu, a freelance translator based in Lucca, Italy who translates Italian to English.

Andreea specializes in many industries, some of which include Information Technology, business/finance, arts/architecture, travel tourism, classical music, journalism, and marketing. She is consistently recognized by her clients and colleagues for not only delivering quality translations, but also providing flexibility, organization, and great communication skills.

Joining the Freelance Community: Transition from Student to Professional

“I’m fascinated by the way I can move ideas and perspectives from one language to another,”  says Joël Dongmo Dontia, a freelancer and a student currently working on getting his Masters in Translation at Université de Dschang in Cameroon.

Joël is using Memsource to help jump start his career as a freelance translator. His determination to excel at school and in the translation market will make him a great addition to the freelance community.

David’s Desk: Machine Translation Post-editing Revisited

It’s been almost six years since we launched Post-editing Analysis, a somewhat revolutionary feature back in 2011 when it was first introduced. Post-editing Analysis measures the post-editing effort based on an edit distance (the machine translation output vs. the final post-edited translation) and introduces the concept of “machine translation matches” that work very much like “translation memory matches”.

Data: Machine and Professional Human Translations Identical in 5 – 20% cases

Click on the chart to enlarge.
Our latest data shows to what extent generic machine translation (MT) engines can help professional translators. Already 5 – 20% of the suggestions from MT are good enough to simply use them as final translation without any changes. Up to 40% of the suggestions are okay after edits, and for 80% of segments MT can provide data to autocomplete.