  Recent works show that discourse analysis benefits from modeling intra- and
inter-sentential levels separately, where proper representations for text units
of different granularities are desired to capture both the meaning of text
units and their relations to the context. In this paper, we propose to take
advantage of transformers to encode contextualized representations of units of
different levels to dynamically capture the information required for discourse
dependency analysis on intra- and inter-sentential levels. Motivated by the
observation of writing patterns commonly shared across articles, we propose a
novel method that treats discourse relation identification as a sequence
labelling task, which takes advantage of structural information from the
context of extracted discourse trees, and substantially outperforms traditional
direct-classification methods. Experiments show that our model achieves
state-of-the-art results on both English and Chinese datasets.
