  While a number of works showed gains from incorporating source-side symbolic
syntactic and semantic structure into neural machine translation (NMT), much
fewer works addressed the decoding of such structure.
  We propose a general Transformer-based approach for tree and graph decoding
based on generating a sequence of transitions, inspired by a similar approach
that uses RNNs by Dyer (2016).
  Experiments with using the proposed decoder with Universal Dependencies
syntax on English-German, German-English and English-Russian show improved
performance over the standard Transformer decoder, as well as over ablated
versions of the model.\tacltxt{\footnote{All code implementing the presented
models will be released upon acceptance.
