  Discourse relations are typically modeled as a discrete class that
characterizes the relation between segments of text (e.g. causal explanations,
expansions). However, such predefined discrete classes limits the universe of
potential relationships and their nuanced differences. Analogous to contextual
word embeddings, we propose representing discourse relations as points in high
dimensional continuous space. However, unlike words, discourse relations often
have no surface form (relations are between two segments, often with no word or
phrase in that gap) which presents a challenge for existing embedding
techniques. We present a novel method for automatically creating discourse
relation embeddings (DiscRE), addressing the embedding challenge through a
weakly supervised, multitask approach to learn diverse and nuanced relations
between discourse segments in social media. Results show DiscRE can: (1) obtain
the best performance on Twitter discourse relation classification task (macro
F1=0.76) (2) improve the state of the art in social media causality prediction
(from F1=.79 to .81), (3) perform beyond modern sentence and contextual word
embeddings at traditional discourse relation classification, and (4) capture
novel nuanced relations (e.g. relations semantically at the intersection of
causal explanations and counterfactuals).
