Voyage en traduction automatique

« Mais enfin, papa, tu ne vas pas aller là ! La traduction automatique, ça marche pas ! c’est pourri …», ainsi s’exprime la fille de l’auteur, 19 ans. Trait générationnel, elle est spontanée, directe.
L’auteur se gratte le front, légèrement ébranlé. Dans la main, il tient sa proposition d’embauche chez le leader mondial de la « machine translation ».

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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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