  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.
