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Existing discourse formalisms use different taxonomies of discourserelations, which require expert knowledge to understand, posing a challenge forannotation and automatic classification. We show that discourse relations canbe effectively captured by some simple cognitively inspired dimensions proposedby Sanders et al.(2018). Our experiments on cross-framework discourse relationclassification (PDTB & RST) demonstrate that it is possible to transferknowledge of discourse relations for one framework to another framework bymeans of these dimensions, in spite of differences in discourse segmentation ofthe two frameworks. This manifests the effectiveness of these dimensions incharacterizing discourse relations across frameworks. Ablation studies revealthat different dimensions influence different types of discourse relations. Thepatterns can be explained by the role of dimensions in characterizing anddistinguishing different relations. We also report our experimental results onautomatic prediction of these dimensions.

Discourse Relations Classification and Cross-Framework Discourse  Relation Classification Through the Lens of Cognitive Dimensions: An  Empirical Investigation