  Learning target side syntactic structure has been shown to improve Neural
Machine Translation (NMT). However, incorporating syntax through latent
variables introduces additional complexity in inference, as the models need to
marginalize over the latent syntactic structures. To avoid this, models often
resort to greedy search which only allows them to explore a limited portion of
the latent space. In this work, we introduce a new latent variable model,
LaSyn, that captures the co-dependence between syntax and semantics, while
allowing for effective and efficient inference over the latent space. LaSyn
decouples direct dependence between successive latent variables, which allows
its decoder to exhaustively search through the latent syntactic choices, while
keeping decoding speed proportional to the size of the latent variable
vocabulary. We implement LaSyn by modifying a transformer-based NMT system and
design a neural expectation maximization algorithm that we regularize with
part-of-speech information as the latent sequences. Evaluations on four
different MT tasks show that incorporating target side syntax with LaSyn
improves both translation quality, and also provides an opportunity to improve
diversity.

Artificial Intelligence;Computation and Language