  Annotation corpus for discourse relations benefits NLP tasks such as machine
translation and question answering. In this paper, we present SciDTB, a
domain-specific discourse treebank annotated on scientific articles. Different
from widely-used RST-DT and PDTB, SciDTB uses dependency trees to represent
discourse structure, which is flexible and simplified to some extent but do not
sacrifice structural integrity. We discuss the labeling framework, annotation
workflow and some statistics about SciDTB. Furthermore, our treebank is made as
a benchmark for evaluating discourse dependency parsers, on which we provide
several baselines as fundamental work.
