Issue #14 – Neural MT: A Case Study

25 Oct18

Author: Dr. John Tinsley, CEO @ Iconic

As a machine translation provider, one of the questions we’ve been asking ourselves most frequently over the last 18 months has been, “When should we switch an existing production deployment to Neural MT?”.

While all new projects are built using Neural MT, there is a certain element of – “if it ain’t broke, don’t fix it”  – that can creep in when it comes to replacing something that’s working well. Despite the promise of improved output and the relative return on investment that will yield, there’s still some level of risk and uncertainty when changing an existing, stable workflow.

That being said, in early 2017, when the opportunity arose for us to test Neural MT on a project that had previously been unsuccessful with Statistical MT, it was the perfect opportunity to put the new technology into practice.

Patent translation for CJK

In a large production workflow, we process roughly 22 million words of patent translation per month from Simplified Chinese and Japanese into English. The output is raw machine translation which is published directly to end-users for information and search.

Strict quality
To finish reading, please visit source site