  Discourse information is difficult to represent and annotate. Among the major
frameworks for annotating discourse information, RST, PDTB and SDRT are widely
discussed and used, each having its own theoretical foundation and focus.
Corpora annotated under different frameworks vary considerably. To make better
use of the existing discourse corpora and achieve the possible synergy of
different frameworks, it is worthwhile to investigate the systematic relations
between different frameworks and devise methods of unifying the frameworks.
Although the issue of framework unification has been a topic of discussion for
a long time, there is currently no comprehensive approach which considers
unifying both discourse structure and discourse relations and evaluates the
unified framework intrinsically and extrinsically. We plan to use automatic
means for the unification task and evaluate the result with structural
complexity and downstream tasks. We will also explore the application of the
unified framework in multi-task learning and graphical models.
