  Utilizing pivot language effectively can significantly improve low-resource
machine translation. Usually, the two translation models, source-pivot and
pivot-target, are trained individually and do not utilize the limited (source,
target) parallel data. This work proposes an end-to-end training method for the
cascaded translation model and configures an improved decoding algorithm. The
input of the pivot-target model is modified to weighted pivot embedding based
on the probability distribution output by the source-pivot model. This allows
the model to be trained end-to-end. In addition, we mitigate the inconsistency
between tokens and probability distributions while using beam search in pivot
decoding. Experiments demonstrate that our method enhances the quality of
translation.
