  Coupled with biaffine decoders, transformers have been effectively adapted to
text-to-graph transduction and achieved state-of-the-art performance on AMR
parsing. Many prior works, however, rely on the biaffine decoder for either or
both arc and label predictions although most features used by the decoder may
be learned by the transformer already. This paper presents a novel approach to
AMR parsing by combining heterogeneous data (tokens, concepts, labels) as one
input to a transformer to learn attention, and use only attention matrices from
the transformer to predict all elements in AMR graphs (concepts, arcs, labels).
Although our models use significantly fewer parameters than the previous
state-of-the-art graph parser, they show similar or better accuracy on AMR 2.0
and 3.0.
