How does Neural Machine Translation work?

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.

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SYSTRAN is far along the NMT path

A Deep Dive into SYSTRAN’s Neural Machine Translation (NMT) Technology

by Kirti Vashee on eMpTy Pages, a blog about translation technology, localization and collaboration

Neural Machine Translation

[…] So, I recently had a conversation with Jean Senellart , Global CTO and SYSTRAN SAS Director General, to find out more about their new NMT technology. He was very forthcoming, and responded to all my questions with useful details, anecdotes and enthusiasm. The conversation only reinforced in my mind that “real MT system development” is something best left to experts, and not something that even large LSPs should dabble with. The reality and complexity of NMT development pushes the limits of MT even further away from the DIY mirage.

In the text below, I have put quotes around everything that I have gotten directly from SYSTRAN material or from Jean Senellart (JAS) to make it clear that I am not interpreting. I have done some minor editing to facilitate readability and “English flow” and added comments in italics within his quotes where this is done.

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