  The addition of syntax-aware decoding in Neural Machine Translation (NMT)
systems requires an effective tree-structured neural network, a syntax-aware
attention model and a language generation model that is sensitive to sentence
structure. We exploit a top-down tree-structured model called DRNN
(Doubly-Recurrent Neural Networks) first proposed by Alvarez-Melis and Jaakola
(2017) to create an NMT model called Seq2DRNN that combines a sequential
encoder with tree-structured decoding augmented with a syntax-aware attention
model. Unlike previous approaches to syntax-based NMT which use dependency
parsing models our method uses constituency parsing which we argue provides
useful information for translation. In addition, we use the syntactic structure
of the sentence to add new connections to the tree-structured decoder neural
network (Seq2DRNN+SynC). We compare our NMT model with sequential and state of
the art syntax-based NMT models and show that our model produces more fluent
translations with better reordering. Since our model is capable of doing
translation and constituency parsing at the same time we also compare our
parsing accuracy against other neural parsing models.
