Abstract
While recent neural machine translation approaches have delivered state-of-the-art performance for resource-rich language pairs, they suffer
from the data scarcity problem for resource-scarce
language pairs. Although this problem can be alleviated by exploiting a pivot language to bridge
the source and target languages, the source-to-pivot
and pivot-to-target translation models are usually
independently trained. In this work, we introduce a
joint training algorithm for pivot-based neural machine translation. We propose three methods to
connect the two models and enable them to interact with each other during training. Experiments on
Europarl and WMT corpora show that joint training
of source-to-pivot and pivot-to-target models leads
to significant improvements over independent training across various languages