  Discourse relations play a pivotal role in establishing coherence within
textual content, uniting sentences and clauses into a cohesive narrative. The
Penn Discourse Treebank (PDTB) stands as one of the most extensively utilized
datasets in this domain. In PDTB-3, the annotators can assign multiple labels
to an example, when they believe that multiple relations are present. Prior
research in discourse relation recognition has treated these instances as
separate examples during training, and only one example needs to have its label
predicted correctly for the instance to be judged as correct. However, this
approach is inadequate, as it fails to account for the interdependence of
labels in real-world contexts and to distinguish between cases where only one
sense relation holds and cases where multiple relations hold simultaneously. In
our work, we address this challenge by exploring various multi-label
classification frameworks to handle implicit discourse relation recognition. We
show that multi-label classification methods don't depress performance for
single-label prediction. Additionally, we give comprehensive analysis of
results and data. Our work contributes to advancing the understanding and
application of discourse relations and provide a foundation for the future
study
