  Text generation from AMR requires mapping a semantic graph to a string that
it annotates. Transformer-based graph encoders, however, poorly capture vertex
dependencies that may benefit sequence prediction. To impose order on an
encoder, we locally constrain vertex self-attention using a graph's tree
decomposition. Instead of forming a full query-key bipartite graph, we restrict
attention to vertices in parent, subtree, and same-depth bags of a vertex. This
hierarchical context lends both sparsity and structure to vertex state updates.
We apply dynamic programming to derive a forest of tree decompositions,
choosing the most structurally similar tree to the AMR. Our system outperforms
a self-attentive baseline by 1.6 BLEU and 1.8 chrF++.
