  Due to its great importance in deep natural language understanding and
various down-stream applications, text-level parsing of discourse rhetorical
structure (DRS) has been drawing more and more attention in recent years.
However, all the previous studies on text-level discourse parsing adopt
bottom-up approaches, which much limit the DRS determination on local
information and fail to well benefit from global information of the overall
discourse. In this paper, we justify from both computational and perceptive
points-of-view that the top-down architecture is more suitable for text-level
DRS parsing. On the basis, we propose a top-down neural architecture toward
text-level DRS parsing. In particular, we cast discourse parsing as a recursive
split point ranking task, where a split point is classified to different levels
according to its rank and the elementary discourse units (EDUs) associated with
it are arranged accordingly. In this way, we can determine the complete DRS as
a hierarchical tree structure via an encoder-decoder with an internal stack.
Experimentation on both the English RST-DT corpus and the Chinese CDTB corpus
shows the great effectiveness of our proposed top-down approach towards
text-level DRS parsing.

Computation and Language