Jost Zetzsche
MultiLingual January/February 2016
Product Reviews

Although it might have gone almost unnoticed in the MT camp, professional translators’ real use of MT is increasingly integrated into existing processes. True, there are still the “traditional” post-editors who work primarily on raw MT, but as any translation vendor who has tried to hire one can tell you, they’re hard to find. Why? Well, it’s a process that the typical translator wasn’t trained for, and it generally doesn’t match the expectation that translators bring to their job. Recognizing both this situation and the existence of valuable data even in publicly available general MT engines, translation environment tool vendors looked at ways to bring that data into the workflow (aside from just displaying full-segment suggestions from machine translation systems that often aren’t particularly helpful)....

In fact, there are too many other creative and productive uses of MT beyond post-editing to list them all here.

Translators and their community have warmly welcomed these developments (though larger language service providers have taken less note because — at least so far — there really is no process in place that allows for measurements and monetization). But they all have one limitation in common: the underlying MT is static. This means two things in our context: the phrase table within the MT is not automatically and immediately updated with the translator’s choices (note that SDL is presently working on a process to account for that), and the automatically generated MT subsegment suggestions come from the initial MT proposal, which does not adjust itself to whatever the translator might already have entered.

Enter Lilt. Lilt uses Phrasal, an open-source statistical machine translation (SMT) system developed by the Stanford Natural Language Processing Group (you can download the source code and find information about it at htpp:// Here’s what distinguishes the way Lilt employs Phrasal from other SMT solutions:...