  Pivot-based neural machine translation (NMT) is commonly used in low-resource
setups, especially for translation between non-English language pairs. It
benefits from using high resource source-pivot and pivot-target language pairs
and an individual system is trained for both sub-tasks. However, these models
have no connection during training, and the source-pivot model is not optimized
to produce the best translation for the source-target task. In this work, we
propose to train a pivot-based NMT system with the reinforcement learning (RL)
approach, which has been investigated for various text generation tasks,
including machine translation (MT). We utilize a non-autoregressive transformer
and present an end-to-end pivot-based integrated model, enabling training on
source-target data.
