  Document-level discourse parsing, in accordance with the Rhetorical Structure
Theory (RST), remains notoriously challenging. Challenges include the deep
structure of document-level discourse trees, the requirement of subtle semantic
judgments, and the lack of large-scale training corpora. To address such
challenges, we propose to exploit robust representations derived from multiple
levels of granularity across syntax and semantics, and in turn incorporate such
representations in an end-to-end encoder-decoder neural architecture for more
resourceful discourse processing. In particular, we first use a pre-trained
contextual language model that embodies high-order and long-range dependency to
enable finer-grain semantic, syntactic, and organizational representations. We
further encode such representations with boundary and hierarchical information
to obtain more refined modeling for document-level discourse processing.
Experimental results show that our parser achieves the state-of-the-art
performance, approaching human-level performance on the benchmarked RST
dataset.
