  Existing discourse corpora are annotated based on different frameworks, which
show significant dissimilarities in definitions of arguments and relations and
structural constraints. Despite surface differences, these frameworks share
basic understandings of discourse relations. The relationship between these
frameworks has been an open research question, especially the correlation
between relation inventories utilized in different frameworks. Better
understanding of this question is helpful for integrating discourse theories
and enabling interoperability of discourse corpora annotated under different
frameworks. However, studies that explore correlations between discourse
relation inventories are hindered by different criteria of discourse
segmentation, and expert knowledge and manual examination are typically needed.
Some semi-automatic methods have been proposed, but they rely on corpora
annotated in multiple frameworks in parallel. In this paper, we introduce a
fully automatic approach to address the challenges. Specifically, we extend the
label-anchored contrastive learning method introduced by Zhang et al. (2022b)
to learn label embeddings during a classification task. These embeddings are
then utilized to map discourse relations from different frameworks. We show
experimental results on RST-DT (Carlson et al., 2001) and PDTB 3.0 (Prasad et
al., 2018).
