Due to the absence of explicit connectives, implicit discourse relation
recognition (IDRR) remains a challenging task in discourse analysis. The
critical step for IDRR is to learn high-quality discourse relation
representations between two arguments. Recent methods tend to integrate the
whole hierarchical information of senses into discourse relation
representations for multi-level sense recognition. Nevertheless, they
insufficiently incorporate the static hierarchical structure containing all
senses (defined as global hierarchy), and ignore the hierarchical sense label
sequence corresponding to each instance (defined as local hierarchy). For the
purpose of sufficiently exploiting global and local hierarchies of senses to
learn better discourse relation representations, we propose a novel GLobal and
LOcal Hierarchy-aware Contrastive Framework (GLOF), to model two kinds of
hierarchies with the aid of contrastive learning. Experimental results on the
PDTB dataset demonstrate that our method remarkably outperforms the current
state-of-the-art model at all hierarchical levels.Comment: 13 pages, 10 figure