  Most semantic parsers that map sentences to graph-based meaning
representations are hand-designed for specific graphbanks. We present a
compositional neural semantic parser which achieves, for the first time,
competitive accuracies across a diverse range of graphbanks. Incorporating BERT
embeddings and multi-task learning improves the accuracy further, setting new
states of the art on DM, PAS, PSD, AMR 2015 and EDS.
