  Complex natural language applications such as speech translation or pivot
translation traditionally rely on cascaded models. However, cascaded models are
known to be prone to error propagation and model discrepancy problems.
Furthermore, there is no possibility of using end-to-end training data in
conventional cascaded systems, meaning that the training data most suited for
the task cannot be used. Previous studies suggested several approaches for
integrated end-to-end training to overcome those problems, however they mostly
rely on (synthetic or natural) three-way data. We propose a cascaded model
based on the non-autoregressive Transformer that enables end-to-end training
without the need for an explicit intermediate representation. This new
architecture (i) avoids unnecessary early decisions that can cause errors which
are then propagated throughout the cascaded models and (ii) utilizes the
end-to-end training data directly. We conduct an evaluation on two pivot-based
machine translation tasks, namely French-German and German-Czech. Our
experimental results show that the proposed architecture yields an improvement
of more than 2 BLEU for French-German over the cascaded baseline.
