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).

翻译：话语关系通常被建模为离散类别，用于表征文本片段间的关系（如因果解释、扩展等）。然而，这种预定义的离散类别限制了潜在关系及其细微差异的可能性。类比于上下文词嵌入，我们提出将话语关系表示为高维连续空间中的点。但与词汇不同，话语关系通常没有表面形式（关系存在于两个片段之间，往往没有明确的词或短语填充其间隙），这对现有嵌入技术构成了挑战。我们提出了一种自动创建话语关系嵌入（DiscRE）的新方法，通过弱监督多任务学习方法应对嵌入挑战，以学习社交媒体中话语片段间多样且细微的关系。结果表明，DiscRE能够：（1）在推特话语关系分类任务中取得最佳性能（宏F1=0.76）；（2）将社交媒体因果关系预测的现有最优结果从F1=0.79提升至0.81；（3）在传统话语关系分类任务中优于现代句子和上下文词嵌入；（4）捕获新颖的细微关系（例如语义上介于因果解释与反事实之间的关系）。