  Abstract Meaning Representation parsing is a sentence-to-graph prediction
task where target nodes are not explicitly aligned to sentence tokens. However,
since graph nodes are semantically based on one or more sentence tokens,
implicit alignments can be derived. Transition-based parsers operate over the
sentence from left to right, capturing this inductive bias via alignments at
the cost of limited expressiveness. In this work, we propose a transition-based
system that combines hard-attention over sentences with a target-side action
pointer mechanism to decouple source tokens from node representations and
address alignments. We model the transitions as well as the pointer mechanism
through straightforward modifications within a single Transformer architecture.
Parser state and graph structure information are efficiently encoded using
attention heads. We show that our action-pointer approach leads to increased
expressiveness and attains large gains (+1.6 points) against the best
transition-based AMR parser in very similar conditions. While using no graph
re-categorization, our single model yields the second best Smatch score on AMR
2.0 (81.8), which is further improved to 83.4 with silver data and ensemble
decoding.
