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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Discover the first issue of Purely Neural Machine Translation insight

PNMT Insight Number 1Project “PNMT” for Purely Neural Machine Translation was this year’s flagship project for the researchers and developers at SYSTRAN.

SYSTRAN brings its expertise in several ways: contributing to research on neural models; applying its know-how in terminology to increase the potential of Neural Machine Translation; and industrializing technology to make it available to companies, organizations and individuals.
We will keep you posted each month and share best practices, research paper, customers insights, product news…

You can download the first issue here.