  While syntactic dependency annotations concentrate on the surface or
functional structure of a sentence, semantic dependency annotations aim to
capture between-word relationships that are more closely related to the meaning
of a sentence, using graph-structured representations. We extend the LSTM-based
syntactic parser of Dozat and Manning (2017) to train on and generate these
graph structures. The resulting system on its own achieves state-of-the-art
performance, beating the previous, substantially more complex state-of-the-art
system by 0.6% labeled F1. Adding linguistically richer input representations
pushes the margin even higher, allowing us to beat it by 1.9% labeled F1.
