  We unify different broad-coverage semantic parsing tasks under a transduction
paradigm, and propose an attention-based neural framework that incrementally
builds a meaning representation via a sequence of semantic relations. By
leveraging multiple attention mechanisms, the transducer can be effectively
trained without relying on a pre-trained aligner. Experiments conducted on
three separate broad-coverage semantic parsing tasks -- AMR, SDP and UCCA --
demonstrate that our attention-based neural transducer improves the state of
the art on both AMR and UCCA, and is competitive with the state of the art on
SDP.
