Abstract
We introduce a novel semantic parsing task
based on Discourse Representation Theory
(DRT; Kamp and Reyle 1993). Our model
operates over Discourse Representation Tree
Structures which we formally define for sentences and documents. We present a general
framework for parsing discourse structures of
arbitrary length and granularity. We achieve
this with a neural model equipped with a supervised hierarchical attention mechanism and
a linguistically-motivated copy strategy. Experimental results on sentence- and documentlevel benchmarks show that our model outperforms competitive baselines by a wide margin.