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.

翻译：篇章关系分类是一项缺乏显式语境标记的特别困难任务（Prasad 等，2008）。当前隐式关系预测方法仅依赖两个相邻目标句子，忽略了其周围环境的更广泛语境（Atwell 等，2021）。在本研究中，我们提出了三种将语境融入句子关系预测任务的新方法：(1) 直接邻居法（DNs），(2) 扩展窗口邻居法（EWNs），以及(3) 部分智能随机邻居法（PSRNs）。我们的研究结果表明，在篇章关系分类任务中，引入超过一个篇章单元的语境信息反而会产生负面影响。