  Non-autoregressive models achieve significant decoding speedup in neural
machine translation but lack the ability to capture sequential dependency.
Directed Acyclic Transformer (DA-Transformer) was recently proposed to model
sequential dependency with a directed acyclic graph. Consequently, it has to
apply a sequential decision process at inference time, which harms the global
translation accuracy. In this paper, we present a Viterbi decoding framework
for DA-Transformer, which guarantees to find the joint optimal solution for the
translation and decoding path under any length constraint. Experimental results
demonstrate that our approach consistently improves the performance of
DA-Transformer while maintaining a similar decoding speedup.
