  Over the past years, interest in discourse analysis and discourse parsing has
steadily grown, and many discourse-annotated corpora and, as a result,
discourse parsers have been built. In this paper, we present a
discourse-annotated corpus for the Persian language built in the framework of
Rhetorical Structure Theory as well as a discourse parser built upon the DPLP
parser, an open-source discourse parser. Our corpus consists of 150
journalistic texts, each text having an average of around 400 words. Corpus
texts were annotated using 18 discourse relations and based on the annotation
guideline of the English RST Discourse Treebank corpus. Our text-level
discourse parser is trained using gold segmentation and is built upon the DPLP
discourse parser, which uses a large-margin transition-based approach to solve
the problem of discourse parsing. The performance of our discourse parser in
span (S), nuclearity (N) and relation (R) detection is around 78%, 64%, 44%
respectively, in terms of F1 measure.
