Issue #42 – Revisiting Interlingua Neural Machine Translation

20 Jun19

Author: Dr. Rohit Gupta, Sr. Machine Translation Scientist @ Iconic

Interlingua Neural MT (NMT) architecture treats the whole NMT system as separate independent blocks per language which can be combined to form a multilingual NMT system. With this architecture we can have a universal source language sentence representation which can be used to translate in any target language. This also enables translation among the languages present in the system even though the system is not specifically trained on that language pair, by way of introducing zero-shot translation.

Recently, Escolano et al. proposed an interlingua NMT architecture with the flexibility to add more languages incrementally, thus avoiding the need to train the whole system again when requiring a new language translation. We had a quick overview of this architecture in our issue #38 and we revisit it again in more detail here.

In Neural MT, an “encoder” is the part of the network that for a given input sentence produces a sentence representation. Analogously, a “decoder” is the part of the network that produces the tokens of the target sentence using the source sentence
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