  It is intuitive that semantic representations can be useful for machine
translation, mainly because they can help in enforcing meaning preservation and
handling data sparsity (many sentences correspond to one meaning) of machine
translation models. On the other hand, little work has been done on leveraging
semantics for neural machine translation (NMT). In this work, we study the
usefulness of AMR (short for abstract meaning representation) on NMT.
Experiments on a standard English-to-German dataset show that incorporating AMR
as additional knowledge can significantly improve a strong attention-based
sequence-to-sequence neural translation model.

Computation and Language;Artificial Intelligence