  Discourse representation tree structure (DRTS) parsing is a novel semantic
parsing task which has been concerned most recently. State-of-the-art
performance can be achieved by a neural sequence-to-sequence model, treating
the tree construction as an incremental sequence generation problem. Structural
information such as input syntax and the intermediate skeleton of the partial
output has been ignored in the model, which could be potentially useful for the
DRTS parsing. In this work, we propose a structural-aware model at both the
encoder and decoder phase to integrate the structural information, where graph
attention network (GAT) is exploited for effectively modeling. Experimental
results on a benchmark dataset show that our proposed model is effective and
can obtain the best performance in the literature.
