e2f and Lilt Case Study: First Large-Scale Application of Auto-Adaptive Machine Translation

Share Article

e2f, a translation and localization company based in San Jose, California, used Palo Alto-based Lilt's auto-adaptive machine translation system to translate 1.77 million words within two weeks for a major travel and leisure site.

Localization engineering design created by e2f utilizing Lilt's auto-adaptive machine translation engine.

e2f’s implementation of Lilt utilized Lilt’s API, plus pre- and post-translation processing and quality checking

Lilt’s productivity gains helped us submit the winning bid on the largest translation project in our company's history, and deliver both ahead of schedule and within budget. — Michel Lopez, CEO, e2f

e2f, of San Jose, California this week released a case study detailing the largest single translation project in the company's fifteen year history: 1.77 million words translated into 6 languages within a ten day turnaround. Such a task could only be accomplished by providing e2f's team of over 100 translators and editors with the auto-adaptive Machine Learning (ML) technology of Palo Alto-based Lilt, Inc.

e2f CEO Michel Lopez noted, "Lilt’s productivity gains helped us submit the winning bid on the largest translation project in our company’s history, and deliver both ahead of schedule and within budget."

Lilt's technology combines Machine Translation (MT) with auto-adaptive Machine Learning (ML), which creates a new paradigm of machine assistance. Such systems learn from the experience, intelligence and insights of their human users, improving productivity by working in partnership, making suggestions and improving accuracy over time.

The net result was that human reviewers produced far higher volumes of content, with nearly the same level of quality, for a fraction of the time and cost. This proved machine assistance could save customers up to one half (or more) of the price of traditional high-quality human translation services.

The results of the case study were made public at LocWorld31 held in Dublin, Ireland, by Spence Green, CEO of Lilt. "This was the first time ever that teams of translators were training an MT system collectively, interactively and in real-time. The project proved that auto-adaptive machine translation technology is ready for large-scale production use," said Green.

e2f, based out of San Jose, California, with over 15 years of success in the translation and localization business, provided the “human capital” for the project. The e2f team was comprised of 100+ experienced translators, editors, and reviewers, plus seven project managers and a senior localization engineer.

Lilt, based out of Palo Alto, California, provided the translation engine for the project. Founded in 2015, its technology platform incorporates the latest research in Natural Language Processing (NLP), Human-Computer Interaction (HCI), and Machine Learning (ML).

You can read the case study in full at https://e2f.com/case-study-lilt-travel-portal/.

Share article on social media or email:

View article via:

Pdf Print

Contact Author

Peter Corless
Follow >
Visit website