Global Language Technology Company thebigword has released a new white paper in collaboration with Xerox and the UK Metropolitan Police, demonstrating how they can achieve human translation quality using Artificial Intelligence and Machine Learning.

WordSynk is the company’s all-in-one language technology platform which is a market first and gives its users the power to communicate without language barriers through a single platform on any networked device.

The platform provides instant access to translation and interpretation services with a network of over 20,000 professional linguists in over 250 language combinations.

Since launching in May 2020, WordSynk now has more than 50,000 users and processes over 90% of all translations and interpretation projects for the company. This equates to the translation of over 1 billion words every single year.

This whitepaper, entitled ‘Neural Machine Translation Reaches Human Parity’ compares WordSynk’s Neural Machine Translation with Human Translation using real-life case studies from the Metropolitan Police and Xerox’s Corporate Marketing Team.

As well as case studies, the whitepaper also includes statistics and data that showcase the demand for Neural Machine Translation and how it has evolved.

Neural Machine Translation’s evolution has seen new developments within the WordSynk platform, with a new recommendation engine that utilises Artificial Intelligence and a Machine Learning algorithm to provide clients with automatic analysis of the best workflow for a translation project.

Chief Operating Officer, Mark Daley said: “We are excited to present this white paper to the public as it demonstrates how WordSynk is changing the localisation game. Global brands to Governments can save time, money and receive content at the same level of quality at a fraction of the cost.

There is no way back. The AI technology behind WordSynk trained by billions of datasets allows the democratisation of translations services.”

The WordSynk Machine Translation whitepaper can be download here.