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