  Discourse relation classification is an especially difficult task without
explicit context markers (Prasad et al., 2008). Current approaches to implicit
relation prediction solely rely on two neighboring sentences being targeted,
ignoring the broader context of their surrounding environments (Atwell et al.,
2021). In this research, we propose three new methods in which to incorporate
context in the task of sentence relation prediction: (1) Direct Neighbors
(DNs), (2) Expanded Window Neighbors (EWNs), and (3) Part-Smart Random
Neighbors (PSRNs). Our findings indicate that the inclusion of context beyond
one discourse unit is harmful in the task of discourse relation classification.
