Lilt Launches First-Ever Adaptive Neural Machine Translation System

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Palo Alto startup is bridging the gap between human and machines with revolutionary translation technology.

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The future of work is bright for the augmented translator.

Translation startup, Lilt, has launched the next generation of human/machine translation: the neural feedback loop. Their adaptive, neural machine translation (NMT) system is the first of its kind in the industry to use artificial intelligence and a real-time feedback loop in order to augment a human translator’s productivity. The system learns from and adapts to its human user enabling efficient, high-quality translation processes. This new technology promises to help close the gap between humans and machines working together towards a future where people can communicate and access information in their own languages.

Lilt’s neural feedback loop for automating translations goes beyond current offerings from Google, Amazon, Facebook, Apple and Microsoft. Meaning that businesses wishing to expand their global reach now have a better option to translate their content quickly and accurately.

Lilt’s NMT system uses the same neural technology that is already being used to advance speech and image recognition, but whose impact on the translation industry is relatively new and promising. In recent months, NMT has been lauded by industry experts for its ability to match the quality of human translation and Lilt's new system is no exception. In Lilt’s neural feedback loop, translators receive context-dependent NMT suggestions while they work. The NMT system passively observes translator preferences to adapt its suggestions in real-time. This creates a virtuous cycle in which translators receive increasingly better suggestions, and the machine receives increasingly better feedback. The neural feedback loop results in higher quality human and machine translation, which helps businesses serve more customers, reduce cost, and shorten time-to-market.

“The future of work is bright for the augmented translator. Translators who use the neural feedback loop produce higher quality translations. This isn’t just good news for the translators, but also for the companies who employ them. It’s a win-win situation for all,” said Spence Green, CEO at Lilt.

In a blind comparison study conducted by Zendesk, a Lilt customer, reviewers were asked to choose between Lilt’s new adaptive NMT translations and Lilt’s previous adaptive machine translation (MT) system. They chose NMT to be of superior or equal quality 71% of the time.

“We love the connection between the human translator and their ability to train our MT engines,” said Melissa Burch, manager of online support at Zendesk. “It meant that when we did make an investment in human translations, it would also contribute to the quality of our MT engines.”

About Lilt
Lilt builds intelligent software to automate translation and optimize among speed, quality, and cost for large-scale localization projects. Lilt is based on machine translation and translator productivity research at Stanford University and Google. Co-founders John DeNero and Spence Green met while working on Google Translate in 2011, and started Lilt in early 2015 to bring the technology to modern businesses and translators. For more information, visit http://www.lilt.com.

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Shermineh Rohanizadeh
Lilt
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