Existing discourse formalisms use different taxonomies of discourse relations, which require expert knowledge to understand, posing a challenge for annotation and automatic classification. We show that discourse relations can be effectively captured by some simple cognitively inspired dimensions proposed by Sanders et al.(2018). Our experiments on cross-framework discourse relation classification (PDTB & RST) demonstrate that it is possible to transfer knowledge of discourse relations for one framework to another framework by means of these dimensions, in spite of differences in discourse segmentation of the two frameworks. This manifests the effectiveness of these dimensions in characterizing discourse relations across frameworks. Ablation studies reveal that different dimensions influence different types of discourse relations. The patterns can be explained by the role of dimensions in characterizing and distinguishing different relations. We also report our experimental results on automatic prediction of these dimensions.

翻译：现有话语形式体系采用不同的话语关系分类体系，这些分类需要专家知识才能理解，给标注和自动分类带来了挑战。我们证明，Sanders等人（2018）提出的若干简洁的认知启发维度能够有效捕捉话语关系。我们在跨框架话语关系分类（PDTB和RST）上的实验表明，尽管两个框架的话语切分存在差异，但通过这些维度可以实现将一个框架的话语关系知识迁移到另一个框架。这证明了这些维度在表征跨框架话语关系方面的有效性。消融研究发现，不同维度对不同类型的话语关系产生影响，这些模式可以通过维度在表征和区分不同关系中的作用加以解释。我们还报告了这些维度的自动预测实验结果。