by Lori Thicke Founder & CEO at Lexcelera
Yesterday I translated our company presentation with Systran’s new Pure Neural™ Machine Translation (PNMT™) engine, and I was amazed at the results.
The presentation in question was a complete overview of all of our services, 59 pages of French text that was edited three separate times to make sure the quality was perfect. (Thanks Faten, Boris and Laurence!)
Then, two days ago, just as I was putting the finishing touches on the presentation for a response to an RFP (Request For Proposals), I found out that our prospective client (a major French manufacturer) wanted our response in English. I had just one day to deliver 59 pages of perfect English content!
Let me give you some background to explain why I, the CEO of a translation company, decided to use Neural Machine Translation for one of our most important commercial documents for one of our most important tenders.
The representation of meaning in Neural, Rule-Based and Phrase-Based Machine Translation
In this issue of step-by-step articles, we explain how neural machine translation (NMT) works and compare it with existing technologies: rule-based engines (RBMT) and phrase-based engines (PBMT, the most popular being Statistical Machine Translation – SMT).
The results obtained from Neural Machine Translation are amazing, in particular, the neural network’s paraphrasing. It almost seems as if the neural network really “understands” the sentence to translate. In this first article, we are interested in “meaning,” that which gives an idea of the type of semantic knowledge the neural networks use to translate.
Let us start with a glimpse of how the 3 technologies work, the different steps of each translation process and the resources that each technology uses to translate. Then we will take a look at a few examples and compare what each technology must do to translate them correctly.