  Parallel corpora are indispensable for training neural machine translation
(NMT) models, and parallel corpora for most language pairs do not exist or are
scarce. In such cases, pivot language NMT can be helpful where a pivot language
is used such that there exist parallel corpora between the source and pivot and
pivot and target languages. Naturally, the quality of pivot language
translation is more inferior to what could be achieved with a direct parallel
corpus of a reasonable size for that pair. In a real-time simultaneous
translation setting, the quality of pivot language translation deteriorates
even further given that the model has to output translations the moment a few
source words become available. To solve this issue, we propose multi-pivot
translation and apply it to a simultaneous translation setting involving pivot
languages. Our approach involves simultaneously translating a source language
into multiple pivots, which are then simultaneously translated together into
the target language by leveraging multi-source NMT. Our experiments in a
low-resource setting using the N-way parallel UN corpus for Arabic to English
NMT via French and Spanish as pivots reveals that in a simultaneous pivot NMT
setting, using two pivot languages can lead to an improvement of up to 5.8
BLEU.
